CN114036330A - Object feature matrix determination method, apparatus, device and storage medium - Google Patents

Object feature matrix determination method, apparatus, device and storage medium Download PDF

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CN114036330A
CN114036330A CN202111234766.2A CN202111234766A CN114036330A CN 114036330 A CN114036330 A CN 114036330A CN 202111234766 A CN202111234766 A CN 202111234766A CN 114036330 A CN114036330 A CN 114036330A
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王越辉
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Xian Wingtech Information Technology Co Ltd
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Abstract

本公开涉及一种对象特征矩阵确定方法、装置、设备和存储介质。前述方法包括:获取对象数据集中各种类型对象的异质特征关联矩阵和对应的关联关系指示矩阵,以及各种类型对象的多层属性异质网络矩阵;基于异质特征关联矩阵和对应的关联关系指示矩阵构建异质特征关联函数;基于多层属性异质网络矩阵构建属性异质网络函数;基于异质特征关联函数和属性异质网络函数,构建最小化目标函数;基于最小化目标函数计算各种类型对象的低秩特征矩阵;采用低秩特征矩阵重构对象数据集中各种类型对象的对象特征矩阵。采用本公开实施例提供的方案,可以降低属性异质网络因同质转换而造成信息损失。

Figure 202111234766

The present disclosure relates to a method, apparatus, device and storage medium for determining an object feature matrix. The aforementioned method includes: acquiring heterogeneous feature association matrices and corresponding association relationship indication matrices of various types of objects in the object data set, and multi-layer attribute heterogeneous network matrices of various types of objects; based on heterogeneous feature association matrices and corresponding associations The relationship indicator matrix constructs the heterogeneous feature correlation function; based on the multi-layer attribute heterogeneous network matrix, the attribute heterogeneous network function is constructed; based on the heterogeneous feature correlation function and the attribute heterogeneous network function, the minimized objective function is constructed; based on the minimized objective function calculation Low-rank feature matrices of various types of objects; low-rank feature matrices are used to reconstruct object feature matrices of various types of objects in the object dataset. By adopting the solutions provided by the embodiments of the present disclosure, the information loss caused by the homogeneous transformation of the attribute heterogeneous network can be reduced.

Figure 202111234766

Description

对象特征矩阵确定方法、装置、设备和存储介质Object feature matrix determination method, apparatus, device and storage medium

技术领域technical field

本公开涉及数据处理技术领域,尤其涉及一种对象特征矩阵确定方法、装置、设备和存储介质。The present disclosure relates to the technical field of data processing, and in particular, to a method, apparatus, device and storage medium for determining an object feature matrix.

背景技术Background technique

对图像、生物分子、社交网站用户等对象进行有效分类依赖于建立有效的图像分类器。在采用判别模型进行对象分类的方法中,构建有效的对象分类器的前提是提取有效地表征样本对象特征的对象特征矩阵。Effective classification of objects such as images, biomolecules, users of social networking sites, etc., relies on building effective image classifiers. In the method of object classification using a discriminant model, the premise of constructing an effective object classifier is to extract an object feature matrix that effectively characterizes the characteristics of the sample object.

相关技术中提出基于矩阵分解确定对象的低秩特征矩阵,基于低秩特征矩阵构建样本对象的对象特征矩阵的方案。基于矩阵分解构建对象特征矩阵的方案可以保证异质数据源的内部结构。但是由于对象数据的不完整性以及模型假设和实验设计的局限性,现有方法仍然存在同质数据转换造成的信息损失及已知异质关联数据不完整的问题。In the related art, a solution is proposed in which a low-rank feature matrix of an object is determined based on matrix decomposition, and an object feature matrix of a sample object is constructed based on the low-rank feature matrix. The scheme of constructing object feature matrix based on matrix decomposition can guarantee the internal structure of heterogeneous data sources. However, due to the incompleteness of object data and the limitations of model assumptions and experimental design, existing methods still suffer from information loss caused by homogeneous data conversion and incomplete known heterogeneous associated data.

发明内容SUMMARY OF THE INVENTION

为了解决上述技术问题或者至少部分地解决上述技术问题,本公开实施例提供了一种对象特征矩阵确定方法、装置、设备和存储介质。In order to solve the above technical problems or at least partially solve the above technical problems, embodiments of the present disclosure provide a method, apparatus, device, and storage medium for determining an object characteristic matrix.

第一方面,本公开实施例提供一种对象特征矩阵确定方法,包括:In a first aspect, an embodiment of the present disclosure provides a method for determining an object feature matrix, including:

获取对象数据集中各种类型对象的异质特征关联矩阵和对应的关联关系指示矩阵,以及各种类型对象的多层属性异质网络矩阵;Obtain the heterogeneous feature correlation matrix and the corresponding correlation indication matrix of various types of objects in the object dataset, as well as the multi-layer attribute heterogeneous network matrix of various types of objects;

基于所述异质特征关联矩阵和对应的所述关联关系指示矩阵构建异质特征关联函数;constructing a heterogeneous feature correlation function based on the heterogeneous feature correlation matrix and the corresponding correlation indication matrix;

基于所述多层属性异质网络矩阵构建属性异质网络函数;constructing an attribute heterogeneous network function based on the multi-layer attribute heterogeneous network matrix;

基于所述异质特征关联函数和所述属性异质网络函数,构建最小化目标函数;based on the heterogeneous feature correlation function and the attribute heterogeneous network function, constructing a minimization objective function;

基于所述最小化目标函数计算各种类型对象的低秩特征矩阵;Calculate low-rank feature matrices of various types of objects based on the minimized objective function;

采用所述低秩特征矩阵重构所述对象数据集中各种类型对象的对象特征矩阵。The low-rank feature matrix is used to reconstruct object feature matrices of various types of objects in the object dataset.

可选地,所述方法还包括:Optionally, the method further includes:

基于所述异质特征关联函数中的第一权重矩阵构建第一约束函数;constructing a first constraint function based on the first weight matrix in the heterogeneous feature correlation function;

基于所述属性异质网络函数中的第二权重矩阵构建第二约束函数;constructing a second constraint function based on the second weight matrix in the attribute heterogeneous network function;

基于所述异质特征关联函数和所述属性异质网络函数,构建最小化目标函数,包括:Based on the heterogeneous feature correlation function and the attribute heterogeneous network function, a minimization objective function is constructed, including:

基于所述异质特征关联函数、所述属性异质网络函数、所述第一约束函数和所述第二约束函数,构建所述最小化目标函数。The minimization objective function is constructed based on the heterogeneous feature correlation function, the attribute heterogeneous network function, the first constraint function and the second constraint function.

可选地,所述方法还包括:Optionally, the method further includes:

还包括:构建未知噪声关联约束函数;Also includes: constructing an unknown noise correlation constraint function;

基于所述异质特征关联函数、所述属性异质网络函数、所述第一约束函数和所述第二约束函数,构建所述最小化目标函数,包括:Based on the heterogeneous feature correlation function, the attribute heterogeneous network function, the first constraint function and the second constraint function, constructing the minimization objective function includes:

基于所述异质特征关联函数、所述属性异质网络函数、所述第一约束函数、所述第二约束函数和所述未知噪声关联约束函数,构建所述最小化目标函数。The minimization objective function is constructed based on the heterogeneous feature correlation function, the attribute heterogeneous network function, the first constraint function, the second constraint function and the unknown noise correlation constraint function.

可选地,所述基于所述最小化目标函数确定各种类型对象的低秩特征矩阵,包括:Optionally, determining low-rank feature matrices of various types of objects based on the minimization objective function, including:

采用交替方向乘子法对所述最小化目标函数进行求解,确定各种类型对象的低秩特征矩阵。The minimization objective function is solved by the alternating direction multiplier method, and the low-rank feature matrix of various types of objects is determined.

所述采用交替方向乘子法对所述最小化目标函数进行求解,包括:The method of using alternating direction multipliers to solve the minimization objective function includes:

采用所述交替方向乘子法依次交替求解所述最小化目标函数中的第一权重矩阵,第二权重矩阵,低秩特征矩阵,异质关联网络矩阵,以及各个多层属性异质网络矩阵的基矩阵,直至达到预设的迭代次数或者收敛。The alternating direction multiplier method is used to alternately solve the first weight matrix, the second weight matrix, the low-rank feature matrix, the heterogeneous correlation network matrix, and the multi-layer attribute heterogeneous network matrix in the minimization objective function. basis matrix until a preset number of iterations or convergence is reached.

可选地,采用所述交替方向乘子法依次交替求解所述最小化目标函数中的第一权重矩阵,第二权重矩阵,低秩特征矩阵,异质关联网络矩阵,以及多层属性异质网络矩阵的基矩阵,包括:Optionally, the alternating direction multiplier method is used to alternately solve the first weight matrix, the second weight matrix, the low-rank feature matrix, the heterogeneous association network matrix, and the multi-layer attribute heterogeneity in the minimization objective function. The basis matrix of the network matrix, including:

设置所述第一权重矩阵、所述第二权重矩阵、所述低秩特征矩阵和所述异质关联网络矩阵为固定值,对所述多层属性异质网络矩阵的基矩阵求第一偏导,在所述第一偏导值为零时,确定所述多层属性异质网络矩阵的基矩阵;Set the first weight matrix, the second weight matrix, the low-rank feature matrix, and the heterogeneous association network matrix as fixed values, and obtain a first bias for the basis matrix of the multi-layer attribute heterogeneous network matrix. derivative, when the first partial derivative value is zero, determine the basis matrix of the multi-layer attribute heterogeneous network matrix;

设置所述第一权重矩阵、所述第二权重矩阵、所述低秩特征矩阵和多层属性异质网络矩阵的基矩阵为固定值,对所述异质关联网络矩阵求第二偏导,在所述第二偏导值为零时,确定所述异质关联网络矩阵;Setting the basis matrix of the first weight matrix, the second weight matrix, the low-rank feature matrix and the multi-layer attribute heterogeneous network matrix as a fixed value, and obtaining a second partial derivative for the heterogeneous association network matrix, When the second partial derivative value is zero, determining the heterogeneous correlation network matrix;

设置所述第一权重矩阵、所述第二权重矩阵、所述异质关联网络矩阵和多层属性异质网络矩阵的基矩阵为固定值,对所述低秩特征矩阵求第三偏导,在所述第三偏导值为零时,确定所述低秩特征矩阵;Setting the base matrices of the first weight matrix, the second weight matrix, the heterogeneous association network matrix and the multi-layer attribute heterogeneous network matrix as a fixed value, and obtaining a third partial derivative for the low-rank feature matrix, When the third partial derivative value is zero, determining the low-rank feature matrix;

设置所述第一权重矩阵、所述低秩特征矩阵、所述异质关联网络矩阵和所述多层属性异质网络矩阵的基矩阵为固定值,对所述第二权重矩阵求第四偏导,在所述第四偏导值为零时,确定所述第二权重矩阵;Set the basis matrix of the first weight matrix, the low-rank feature matrix, the heterogeneous association network matrix, and the multi-layer attribute heterogeneous network matrix as a fixed value, and obtain a fourth bias for the second weight matrix. derivative, when the fourth partial derivative value is zero, determine the second weight matrix;

设置所述第二权重矩阵、所述低秩特征矩阵、所述异质关联网络矩阵和所述多层属性异质网络矩阵的基矩阵为固定值,对所述第一权重矩阵求第五偏导,在所述第五偏导值为零时,确定所述第一权重矩阵。Set the base matrix of the second weight matrix, the low-rank feature matrix, the heterogeneous association network matrix, and the multi-layer attribute heterogeneous network matrix as a fixed value, and obtain a fifth bias for the first weight matrix The first weight matrix is determined when the value of the fifth partial derivative is zero.

可选地,所述对象数据集为图像数据集,所述对象数据集中的对象为图像。Optionally, the object dataset is an image dataset, and the objects in the object dataset are images.

第二方面,本公开实施例提供一种对象特征矩阵确定装置,包括:In a second aspect, an embodiment of the present disclosure provides an apparatus for determining an object characteristic matrix, including:

矩阵获取单元,用于获取对象数据集中各种类型对象的异质特征关联矩阵和对应的关联关系指示矩阵,以及各种类型对象的多层属性异质网络矩阵;The matrix acquisition unit is used to acquire the heterogeneous feature correlation matrix of various types of objects in the object dataset and the corresponding correlation indication matrix, as well as the multi-layer attribute heterogeneous network matrix of various types of objects;

函数构建单元,用于基于所述异质特征关联矩阵和对应的所述关联关系指示矩阵构建异质特征关联函数;基于所述多层属性异质网络矩阵构建属性异质网络函数;以及,基于所述异质特征关联函数和所述属性异质网络函数,构建最小化目标函数;a function construction unit, configured to construct a heterogeneous feature correlation function based on the heterogeneous feature correlation matrix and the corresponding correlation indication matrix; build an attribute heterogeneous network function based on the multi-layer attribute heterogeneous network matrix; and, based on The heterogeneous feature correlation function and the attribute heterogeneous network function are used to construct a minimization objective function;

求解单元,用于基于所述最小化目标函数计算各种类型对象的低秩特征矩阵;a solving unit for calculating low-rank feature matrices of various types of objects based on the minimization objective function;

对象特征矩阵确定单元,用于采用所述低秩特征矩阵重构所述对象数据集中各种类型对象的对象特征矩阵。An object feature matrix determining unit, configured to use the low-rank feature matrix to reconstruct object feature matrices of various types of objects in the object dataset.

第三方面,本公开实施例提供一种电子设备,包括:In a third aspect, an embodiment of the present disclosure provides an electronic device, including:

一个或多个处理器;one or more processors;

存储装置,用于存储一个或多个程序,storage means for storing one or more programs,

当所述一个或多个程序被所述一个或多个处理器执行,使得所述一个或多个处理器实现如第一方面中任一所述的方法。The one or more programs, when executed by the one or more processors, cause the one or more processors to implement a method as described in any one of the first aspects.

第四方面,本公开实施例提供一种计算机可读存储介质,其上存储有计算机程序,其特征在于,该程序被处理器执行时实现如权第一方面中任一所述的方法。In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium on which a computer program is stored, characterized in that, when the program is executed by a processor, the method according to any one of the first aspects is implemented.

本公开实施例提供的技术方案与现有技术相比具有如下优点:Compared with the prior art, the technical solutions provided by the embodiments of the present disclosure have the following advantages:

本公开实施提供的方案,通过异质特征关联矩阵和对应的关联关系指示矩阵构建异质特征关联函数,以实现利用关联关系指示矩阵对异质的异质关联和潜在的异质关联进行区分。通过采用各种类型对象对应的低秩特征矩阵和各种类型对象的属性异质网络分解后的基矩阵构建属性异质网络函数,并基于异质特征关联函数和属性异质网络函数构建最小化目标函数,利用最小化目标函数对异质特征关联函数和属性异质网络函数中的第一权重矩阵、低秩特征矩阵、异质关联网络矩阵、第二权重矩阵和各种类型对象的属性异质网络分解后的基矩阵协同分级,而获得各种类型对象的低秩特征矩阵,并基于各种类型对象的低秩特征矩阵确定各种类型对象的对象特征矩阵。采用本公开实施例提供的方案,可以使得网络的拓扑结构、节点的属性信息融合在一起,从而弥补已知关联不足造成的冷启动问题,进而降低了属性异质网络因同质转换而造成信息损失。The present disclosure implements the solution provided by constructing a heterogeneous feature correlation function through a heterogeneous feature correlation matrix and a corresponding correlation relationship indication matrix, so as to realize the use of the correlation relationship indicator matrix to distinguish heterogeneous heterogeneous correlations and potential heterogeneous correlations. The attribute heterogeneous network function is constructed by using the low-rank feature matrix corresponding to various types of objects and the decomposed basis matrix of the attribute heterogeneous network of various types of objects. The objective function is to use the objective function to minimize the first weight matrix, low-rank feature matrix, heterogeneous correlation network matrix, second weight matrix and the attribute difference of various types of objects in the heterogeneous feature correlation function and the attribute heterogeneous network function. The basis matrices decomposed by the qualitative network are co-graded to obtain low-rank feature matrices of various types of objects, and the object feature matrices of various types of objects are determined based on the low-rank feature matrices of various types of objects. By adopting the solution provided by the embodiments of the present disclosure, the topology structure of the network and the attribute information of the nodes can be fused together, so as to make up for the cold start problem caused by the lack of known associations, thereby reducing the information caused by the homogeneous transformation of the heterogeneous network with attributes. loss.

附图说明Description of drawings

此处的附图被并入说明书中并构成本说明书的一部分,示出了符合本公开的实施例,并与说明书一起用于解释本公开的原理。The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description serve to explain the principles of the disclosure.

为了更清楚地说明本公开实施例或现有技术中的技术方案,下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍,显而易见地,对于本领域普通技术人员而言,在不付出创造性劳动性的前提下,还可以根据这些附图获得其他的附图。In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the accompanying drawings that are required to be used in the description of the embodiments or the prior art will be briefly introduced below. In other words, on the premise of no creative labor, other drawings can also be obtained from these drawings.

图1是本公开实施例提供的对象特征矩阵确定方法流程图;1 is a flowchart of a method for determining an object feature matrix provided by an embodiment of the present disclosure;

图2是本公开实施例提供的一种对象特征矩阵确定的方法流程图;2 is a flowchart of a method for determining an object feature matrix provided by an embodiment of the present disclosure;

图3是本公开实施例提供的一种对象特征矩阵确定的方法流程图;3 is a flowchart of a method for determining an object feature matrix provided by an embodiment of the present disclosure;

图4是本公开实施例提供的一种对象确定装置的结构示意图;FIG. 4 is a schematic structural diagram of an object determination apparatus provided by an embodiment of the present disclosure;

图5是本公开实施例提供的一种电子设备的结构示意图。FIG. 5 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure.

具体实施方式Detailed ways

为了能够更清楚地理解本公开的上述目的、特征和优点,下面将对本公开的方案进行进一步描述。需要说明的是,在不冲突的情况下,本公开的实施例及实施例中的特征可以相互组合。In order to more clearly understand the above objects, features and advantages of the present disclosure, the solutions of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features in the embodiments may be combined with each other under the condition of no conflict.

在下面的描述中阐述了很多具体细节以便于充分理解本公开,但本公开还可以采用其他不同于在此描述的方式来实施;显然,说明书中的实施例只是本公开的一部分实施例,而不是全部的实施例。Many specific details are set forth in the following description to facilitate a full understanding of the present disclosure, but the present disclosure can also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only a part of the embodiments of the present disclosure, and Not all examples.

本公开实施例提供一种对象特征矩阵确定方法,用于解决现有基于矩阵分解确定对象特征的方法在融合(也就是矩阵分解)过程中因同质数据转换造成的信息损失,以及已知异质数据关联不完整的问题,继而更为准确地表征对象数据集中对象的特征。具体实施例中,本公开实施例中的对象可以是图像、生物分子、社交网站用户等类型的对象。The embodiments of the present disclosure provide a method for determining an object feature matrix, which is used to solve the information loss caused by the conversion of homogeneous data during the fusion (that is, matrix decomposition) process of the existing method for determining object features based on matrix decomposition, and the known differences It solves the problem of incomplete correlation of qualitative data, and then more accurately characterizes the characteristics of objects in the object dataset. In specific embodiments, the objects in the embodiments of the present disclosure may be objects of types such as images, biomolecules, users of social networking sites, and the like.

其中,本公开的对象特征矩阵确定方法由电子设备或者电子设备中的应用程序等来执行。电子设备可以是平板电脑、手机、笔记本电脑、服务器等设备,本公开对电子设备的具体类型不作任何限制。本公开对电子设备的操作系统的类型不做限定。例如,Android系统、Linux系统、Windows系统、iOS系统等。Wherein, the object feature matrix determination method of the present disclosure is executed by an electronic device or an application program in the electronic device or the like. The electronic device may be a tablet computer, a mobile phone, a notebook computer, a server and other devices, and the present disclosure does not impose any limitation on the specific type of the electronic device. The present disclosure does not limit the type of the operating system of the electronic device. For example, Android system, Linux system, Windows system, iOS system, etc.

图1是本公开实施例提供的对象特征矩阵确定方法流程图。如图1所示,本公开实施例提供的对象特征矩阵确定方法包括步骤S101-S106。FIG. 1 is a flowchart of a method for determining an object feature matrix provided by an embodiment of the present disclosure. As shown in FIG. 1 , the method for determining an object feature matrix provided by an embodiment of the present disclosure includes steps S101-S106.

步骤S101:获取对象数据集中各种类型对象的异质特征关联矩阵和对应的关联关系指示矩阵,以及各种类型对象的多层属性异质网络矩阵。Step S101: Acquire the heterogeneous feature correlation matrix of various types of objects in the object data set and the corresponding correlation relationship indication matrix, and the multi-layered attribute heterogeneous network matrix of various types of objects.

本公开实施例中,电子设备可以对对象数据集中各种类型对象的原始数据进行处理,获取各种类型对象的异质特征关联矩阵、对应的关联关系指示矩阵和多层属性异质网络矩阵,也可以通过读取存储中预先存储的数据,获取前述矩阵。In the embodiment of the present disclosure, the electronic device can process the original data of various types of objects in the object data set, and obtain the heterogeneous feature correlation matrix, the corresponding correlation indication matrix and the multi-layer attribute heterogeneous network matrix of various types of objects, The aforementioned matrix can also be obtained by reading pre-stored data in storage.

本公开实施例采用

Figure BDA0003317325330000061
表示第i种类型对象的ni个样本之间的异质特征关联矩阵,采用Hij表示与Rij对应的并且维度同Rij相同的关联关系指示矩阵。其中关联关系指示矩阵Hij用于将一致特征关联关系矩阵中的观察到的关联关系和未观察到的关联关系区分开。具体的,如果Rij(s,t)>0,那么Hij=1,否则Hij=0。The embodiment of the present disclosure adopts
Figure BDA0003317325330000061
Represents the heterogeneous feature correlation matrix between n i samples of the i-th type of object, using H ij to represent the correlation indicating matrix corresponding to R ij and having the same dimension as R ij . The correlation indicator matrix H ij is used to distinguish the observed correlation from the unobserved correlation in the consistent feature correlation matrix. Specifically, if Rij (s,t)>0, then Hij =1, otherwise Hij =0.

本公开实施例采用

Figure BDA0003317325330000062
表示第i种类型对象的多种属性异质网络矩阵,其中ti表示第i种类型对象共收集到ti种来源的属性异质网络,dit表示第t种属性异质网络有dit种属性。The embodiment of the present disclosure adopts
Figure BDA0003317325330000062
Represents the heterogeneous network matrix of various attributes of the ith type of object, where t i represents the attribute heterogeneous network of t i sources collected by the ith type of object, and d it indicates that the t-th attribute heterogeneous network has d it a property.

步骤S102:基于异质特征关联矩阵和对应的关联关系指示矩阵构建异质特征关联函数。Step S102 : constructing a heterogeneous feature correlation function based on the heterogeneous feature correlation matrix and the corresponding correlation relationship indication matrix.

异质特征关联函数中除了包括前述的异质特征关联矩阵和对应的关联管理指示矩阵外,还包括第一权重矩阵、各种类型对象对应的低秩特征矩阵和各种类型对象之间的异质关联网络矩阵。In addition to the aforementioned heterogeneous feature correlation matrix and the corresponding correlation management indicator matrix, the heterogeneous feature correlation function also includes a first weight matrix, a low-rank feature matrix corresponding to various types of objects, and the heterogeneity between various types of objects. The qualitative correlation network matrix.

在本公开的一个实施例中,异质特征关联函数采用公式1.1表示。In an embodiment of the present disclosure, the heterogeneous feature correlation function is represented by formula 1.1.

Figure BDA0003317325330000071
Figure BDA0003317325330000071

其中:e表示哈达玛积;

Figure BDA0003317325330000072
表示第i种或第j种类型对象之间的异构关联网络指导的低秩特征矩阵,两个低秩标识矩阵分别表示了在压缩的ki,kj维空间以实数化的形式描述第i种类型对象的ni个对象的属性信息和第j种类型对象的nj个对象的属性信息;ki和kj分别表示第i种或者第j种低秩特征矩阵的维度;
Figure BDA0003317325330000073
表示了异质关联网络矩阵,其相比于Rij规模小很多;
Figure BDA00033173253300000711
表示对
Figure BDA0003317325330000074
个异质关联网络所赋予的第一权重矩阵,对于
Figure BDA0003317325330000075
Figure BDA0003317325330000076
可近似为重构损失并被用于区别已观察到的关联关系和未观察到的关联关系,并使得已观察到的关联在重构后的Rij中得以保持。Among them: e represents the Hadamard product;
Figure BDA0003317325330000072
Represents the low-rank feature matrix guided by the heterogeneous association network between the i-th or j-th types of objects, and the two low-rank identification matrices respectively represent the compressed ki, k j -dimensional space to describe the i-th in the form of real numbers The attribute information of n i objects of the type of object and the attribute information of the n j objects of the j-th type of object; k i and k j respectively represent the dimension of the i-th or j-th low-rank feature matrix;
Figure BDA0003317325330000073
represents the heterogeneous correlation network matrix, which is much smaller than R ij ;
Figure BDA00033173253300000711
express right
Figure BDA0003317325330000074
The first weight matrix assigned by a heterogeneous association network, for
Figure BDA0003317325330000075
Figure BDA0003317325330000076
can be approximated as a reconstruction loss and is used to distinguish between observed and unobserved associations, and allows the observed associations to be preserved in the reconstructed Rij .

步骤S103:基于多层属性异质网络矩阵构建属性异质网络函数。Step S103 : constructing an attribute heterogeneity network function based on the multi-layer attribute heterogeneity network matrix.

本公开实施例中,属性异质网络函数除了包括前述的多种属性异质网络矩阵外,还包括第二权重矩阵、各种类型对象对应的低秩特征矩阵和各种类型对象的属性异质网络分解后的基矩阵。In the embodiment of the present disclosure, the attribute heterogeneity network function includes, in addition to the aforementioned various attribute heterogeneity network matrices, a second weight matrix, a low-rank feature matrix corresponding to various types of objects, and attribute heterogeneity of various types of objects The basis matrix after network decomposition.

在本公开的一个实施例中,属性异质网络函数采用公式1.2表示。In an embodiment of the present disclosure, the attribute heterogeneity network function is represented by formula 1.2.

Figure BDA0003317325330000077
Figure BDA0003317325330000077

其中,

Figure BDA0003317325330000078
表示对多种对象的属性异质网络矩阵所赋予的第二权重矩阵,对于Xit,如果t>maxiti,
Figure BDA0003317325330000079
Gi表示第i种类型对象的属性异质网络指导的低秩特征矩阵;
Figure BDA00033173253300000710
表示第i种类型对象的属性异质网络分解后的基矩阵。in,
Figure BDA0003317325330000078
Represents the second weight matrix assigned to the attribute heterogeneous network matrix of various objects, for X it , if t>max i t i ,
Figure BDA0003317325330000079
G i represents the low-rank feature matrix guided by the attribute heterogeneous network of the i-th type of object;
Figure BDA00033173253300000710
Represents the basis matrix after the decomposition of the attribute heterogeneous network of the ith type of object.

本公开实施例中,采用属性异质网络函数可以直接将属性异构数据进行分解,避免同质转换造成的信息损失。In the embodiment of the present disclosure, the attribute heterogeneous network function can be used to directly decompose the attribute heterogeneous data, so as to avoid information loss caused by homogeneous transformation.

步骤S104:基于异质特征关联函数和属性异质网络函数,构建最小化目标函数。Step S104: Construct a minimization objective function based on the heterogeneous feature correlation function and the attribute heterogeneous network function.

本公司实施例中,基于异质特征关联函数和属性异质网络函数构建最小化目标函数,可以是将异质特征关联函数和属性异质网络函数相加。In the embodiment of the company, the minimization objective function is constructed based on the heterogeneous feature correlation function and the attribute heterogeneous network function, which may be the addition of the heterogeneous feature correlation function and the attribute heterogeneous network function.

在本公开的一个实施例中,在将异质特征关联函数和属性异质网络函数相加后,得到的最小化目标函数采用公式1.3表示In an embodiment of the present disclosure, after adding the heterogeneous feature correlation function and the attribute heterogeneous network function, the obtained minimization objective function is expressed by formula 1.3

Figure BDA0003317325330000081
Figure BDA0003317325330000081

s.t.ωr≥0,ωh≥0,∑vec(ωr)=1,∑vec(ωh)=1 (1.3)stω r ≥0, ω h ≥0, ∑vec(ω r )=1, ∑vec(ω h )=1 (1.3)

步骤S105:基于最小化目标函数计算各种类型对象的低秩特征矩阵。Step S105: Calculate low-rank feature matrices of various types of objects based on the minimization objective function.

本公开实施例中,基于最小化目标函数计算各种类型对象的低秩特征矩阵是对最小化目标函数中的第一权重矩阵、各种类型对象对应的低秩特征矩阵、各种类型对象之间的异质关联网络矩阵、第二权重矩阵、各种类型对象的属性异质网络分解后的基矩阵进行协同分解,并在协同分解过程中获取得到各种类型对象的低秩特征矩阵G。In this embodiment of the present disclosure, calculating low-rank feature matrices of various types of objects based on the minimization objective function is the calculation of the first weight matrix in the minimized objective function, the low-rank feature matrices corresponding to various types of objects, and the difference between the various types of objects. The heterogeneous correlation network matrix, the second weight matrix, and the basis matrix after the heterogeneous network decomposition of the attributes of various types of objects are decomposed collaboratively, and the low-rank feature matrix G of various types of objects is obtained in the process of co-decomposition.

本公开实施例中,最小化目标函数在G,S,U,ωrh上是非凸的,因此可以借助被用于近似三因子矩阵分解的交替方向乘子法(Alternating Direction Method ofMultipliers,ADMM)对其进行优化求解。具体的,可以将G,S,U,ωrh中的四个参数设为常数,同时优化另一个,反复迭代,直至所有的参数都求解完成。具体采用交替方向乘子法计算G,S,U,ωrh在下文中在做表述。In the embodiment of the present disclosure, the minimization objective function is non-convex on G, S, U, ω r , ω h , so the Alternating Direction Method of Multipliers (Alternating Direction Method of Multipliers, ADMM) to optimize it. Specifically, four parameters in G, S, U, ω r , ω h can be set as constants, and the other one can be optimized at the same time, and iteratively iterate until all the parameters are solved. Specifically, the calculation of G, S, U, ω r , ω h by the alternate direction multiplier method is described below.

步骤S106:采用低秩特征矩阵重构对象数据集中各种类型对象的对象特征矩阵。Step S106: Reconstructing object feature matrices of various types of objects in the object dataset by using the low-rank feature matrix.

在确定各种类型对象的低秩特征矩阵后,可以采用低秩特征矩阵重构对象特征矩阵。在本公开一些实施例中,在确定各种类型对象的低秩特征矩阵后,可以直接将低秩特征矩阵作为对应类型对象的对象特征矩阵。After determining the low-rank feature matrices of various types of objects, the low-rank feature matrices can be used to reconstruct the object feature matrices. In some embodiments of the present disclosure, after determining low-rank feature matrices of various types of objects, the low-rank feature matrices may be directly used as object feature matrices of corresponding types of objects.

本公开实施提供的对象特征矩阵确定方法,通过异质特征关联矩阵和对应的关联关系指示矩阵构建异质特征关联函数,以实现利用关联关系指示矩阵对异质的异质关联和潜在的异质关联进行区分。通过采用各种类型对象对应的低秩特征矩阵和各种类型对象的属性异质网络分解后的基矩阵构建属性异质网络函数,并基于异质特征关联函数和属性异质网络函数构建最小化目标函数,利用最小化目标函数对异质特征关联函数和属性异质网络函数中的第一权重矩阵、低秩特征矩阵、异质关联网络矩阵、第二权重矩阵和各种类型对象的属性异质网络分解后的基矩阵协同分级,而获得各种类型对象的低秩特征矩阵,并基于各种类型对象的低秩特征矩阵确定各种类型对象的对象特征矩阵。The present disclosure implements the object feature matrix determination method provided by the heterogeneous feature correlation matrix and the corresponding correlation indication matrix to construct a heterogeneous feature correlation function, so as to realize the heterogeneous correlation and potential heterogeneity using the correlation indication matrix. Association to distinguish. The attribute heterogeneous network function is constructed by using the low-rank feature matrix corresponding to various types of objects and the decomposed basis matrix of the attribute heterogeneous network of various types of objects. The objective function is to use the objective function to minimize the first weight matrix, low-rank feature matrix, heterogeneous correlation network matrix, second weight matrix and the attribute difference of various types of objects in the heterogeneous feature correlation function and the attribute heterogeneous network function. The basis matrices decomposed by the qualitative network are co-graded to obtain low-rank feature matrices of various types of objects, and the object feature matrices of various types of objects are determined based on the low-rank feature matrices of various types of objects.

采用本公开实施例提供的方法,可以使得网络的拓扑结构、节点的属性信息融合在一起,从而弥补已知关联不足造成的冷启动问题,进而降低了属性异质网络因同质转换而造成信息损失。By using the method provided by the embodiments of the present disclosure, the topology structure of the network and the attribute information of the nodes can be fused together, so as to make up for the cold start problem caused by the lack of known associations, thereby reducing the information caused by the homogeneous transformation of the heterogeneous network with attributes. loss.

图2是本公开实施例提供的一种对象特征矩阵确定的方法流程图。如图2所示,本公开实施例提供的方法包括步骤S201-S207。FIG. 2 is a flowchart of a method for determining an object feature matrix provided by an embodiment of the present disclosure. As shown in FIG. 2 , the method provided by the embodiment of the present disclosure includes steps S201-S207.

步骤S201:获取对象数据集中各种类型对象的异质特征关联矩阵和对应的关联关系指示矩阵,以及各种类型对象的多层属性异质网络矩阵。Step S201: Obtain the heterogeneous feature correlation matrix and the corresponding correlation relationship indication matrix of various types of objects in the object data set, and the multi-layer attribute heterogeneous network matrix of various types of objects.

步骤S202:基于异质特征关联矩阵和对应的关联关系指示矩阵构建异质特征关联函数。Step S202 : constructing a heterogeneous feature correlation function based on the heterogeneous feature correlation matrix and the corresponding correlation relationship indication matrix.

步骤S203:基于多层属性异质网络矩阵构建属性异质网络函数。Step S203 : constructing an attribute heterogeneity network function based on the multi-layer attribute heterogeneity network matrix.

前述步骤S201-S203与前文实施例步骤S101-S103步骤相同,具体可以参见前文实施例,此处不再复述。The foregoing steps S201-S203 are the same as the steps S101-S103 in the foregoing embodiment. For details, refer to the foregoing embodiment, which will not be repeated here.

步骤S204:基于异质特征关联函数中的第一权重矩阵构建第一约束函数;以及,基于属性异质网络函数中的第二权重矩阵构建第二约束函数。Step S204 : constructing a first constraint function based on the first weight matrix in the heterogeneous feature correlation function; and constructing a second constraint function based on the second weight matrix in the attribute heterogeneous network function.

步骤S205:基于异质特征关联函数、属性异质网络函数、第一约束函数和第二约束函数,构建最小化目标函数。Step S205 : constructing a minimization objective function based on the heterogeneous feature correlation function, the attribute heterogeneous network function, the first constraint function and the second constraint function.

在对公式1.3所示的最小化目标函数进行求解时,当Rij具有最小的近似损失

Figure BDA0003317325330000101
时,Rij的权重将为
Figure BDA0003317325330000102
此时其他异质关联矩阵将全都被忽略。同样的,当Xit具有最少的近似损失
Figure BDA0003317325330000103
此时倾向于将
Figure BDA0003317325330000104
分配给Xit。也就是说,所有其他的同质矩阵的贡献都会被忽略。When solving the minimization objective function shown in Equation 1.3, when R ij has the smallest approximation loss
Figure BDA0003317325330000101
, the weight of R ij will be
Figure BDA0003317325330000102
At this point the other heterogeneous correlation matrices will all be ignored. Likewise, when X it has the least approximation loss
Figure BDA0003317325330000103
tend to be
Figure BDA0003317325330000104
assigned to X it . That is, the contributions of all other homogeneous matrices are ignored.

同时大量的研究已经证实,不同的数据源彼此之间可以提供补充信息。因此,仅使用单个的异质关联矩阵和单个的同质关联矩阵可能无法给出可靠的预测。At the same time, numerous studies have confirmed that different data sources can provide complementary information to each other. Therefore, using only a single heterogeneous correlation matrix and a single homogeneous correlation matrix may not give reliable predictions.

为了弥补前述缺陷的权重分配,本公开实施例中首先基于异质特征关联函数中的第一权重矩阵构建第一约束函数,以及基于属性异质网络函数中的第二权重矩阵构建第二约束函数。In order to make up for the aforementioned defective weight assignment, in the embodiment of the present disclosure, a first constraint function is first constructed based on the first weight matrix in the heterogeneous feature correlation function, and a second constraint function is constructed based on the second weight matrix in the attribute heterogeneous network function. .

本公开实施例中,第一约束函数和第二约束函数均为基于l2范数的正则项。第一约束函数为

Figure BDA0003317325330000105
第二约束函数为
Figure BDA0003317325330000106
前述vec(ωr)是将ωr的行堆叠拼接后的向量,vec(ωh)是将ωh的行堆叠拼接后的向量。α>0,β>0被用来控制vec(ωr)和vec(ωh)的复杂度。同时,α,β还可以帮助选择性的集成不同的异质关联数据源和属性异质数据源。In the embodiment of the present disclosure, both the first constraint function and the second constraint function are regular terms based on the l2 norm. The first constraint function is
Figure BDA0003317325330000105
The second constraint function is
Figure BDA0003317325330000106
The aforementioned vec(ω r ) is a vector obtained by stacking and splicing rows of ω r , and vec(ω h ) is a vector obtained by stacking and splicing rows of ω h . α>0, β>0 are used to control the complexity of vec(ω r ) and vec(ω h ). At the same time, α, β can also help to selectively integrate different heterogeneous association data sources and attribute heterogeneous data sources.

在添加第一约束函数和第二约束函数的情况下,本公开实施例中得到的最小化目标函数采用公式1.4表示。In the case of adding the first constraint function and the second constraint function, the minimized objective function obtained in the embodiment of the present disclosure is expressed by formula 1.4.

Figure BDA0003317325330000107
Figure BDA0003317325330000107

步骤S206:基于最小化目标函数计算各种类型对象的低秩特征矩阵。Step S206: Calculate low-rank feature matrices of various types of objects based on the minimization objective function.

步骤S207:采用所述低秩特征矩阵重构所述对象数据集中各种类型对象的对象特征矩阵。Step S207: Reconstructing object feature matrices of various types of objects in the object dataset by using the low-rank feature matrix.

前述步骤S206-S207与前文实施例步骤S105-S106步骤相同,具体可以参见前文实施例,此处不再复述。The foregoing steps S206-S207 are the same as the steps S105-S106 in the foregoing embodiment. For details, refer to the foregoing embodiment, which will not be repeated here.

本公开实施例提供的对象特征矩阵确定方法,基于异质特征关联函数中的第一权重矩阵构建第一约束函数,以及基于属性异质网络函数中的第二权重矩阵构建第二约束函数,通过第一约束函数和第二约束函数与异质特征关联函数、属性异质网络函数构建最小化目标函数,并基于最小化目标函数计算得到各个对象的特征,弥补了仅使用单个的异质关联矩阵和单个的同质关联矩阵可能无法给出可靠的预测的缺陷。In the method for determining an object feature matrix provided by the embodiment of the present disclosure, a first constraint function is constructed based on a first weight matrix in a heterogeneous feature correlation function, and a second constraint function is constructed based on a second weight matrix in the attribute heterogeneous network function, through The first constraint function and the second constraint function construct the minimized objective function with the heterogeneous feature correlation function and the attribute heterogeneous network function, and calculate the characteristics of each object based on the minimized objective function, which makes up for the use of only a single heterogeneous correlation matrix. and a single homogeneous correlation matrix may not give reliable predictions for the flaw.

图3是本公开实施例提供的一种对象确定的方法流程图。如图3所示,本公开实施例提供的方法包括步骤S301-S308。FIG. 3 is a flowchart of a method for object determination provided by an embodiment of the present disclosure. As shown in FIG. 3 , the method provided by the embodiment of the present disclosure includes steps S301-S308.

步骤S301:获取对象数据集中各种类型对象的异质特征关联矩阵和对应的关联关系指示矩阵,以及各种类型对象的多层属性异质网络矩阵。Step S301: Obtain the heterogeneous feature correlation matrix and the corresponding correlation relationship indication matrix of various types of objects in the object data set, and the multi-layer attribute heterogeneous network matrix of various types of objects.

步骤S302:基于异质特征关联矩阵和对应的关联关系指示矩阵构建异质特征关联函数。Step S302: Construct a heterogeneous feature correlation function based on the heterogeneous feature correlation matrix and the corresponding correlation relationship indication matrix.

步骤S303:基于多层属性异质网络矩阵构建属性异质网络函数。Step S303 : constructing an attribute heterogeneity network function based on the multi-layer attribute heterogeneity network matrix.

步骤S304:基于异质特征关联函数中的第一权重矩阵构建第一约束函数;以及,基于属性异质网络函数中的第二权重矩阵构建第二约束函数。Step S304 : constructing a first constraint function based on the first weight matrix in the heterogeneous feature correlation function; and constructing a second constraint function based on the second weight matrix in the attribute heterogeneous network function.

前述步骤S301-S304与前文实施例步骤S201-S204步骤相同,具体可以参见前文实施例,此处不再复述。The foregoing steps S301-S304 are the same as the steps S201-S204 in the foregoing embodiment. For details, refer to the foregoing embodiment, which will not be repeated here.

步骤S305:构建未知噪声关联约束函数。Step S305: Construct an unknown noise correlation constraint function.

具体实施例中,关联关系指示矩阵Hij将已观察到的关联关系和未观察到的关联关系进行区分,但是仅对已观察到的关联进行约束,从而很可能在某种程度上随机留下大量未观察的关联未加约束,进而导致目标关联矩阵中出现大量噪声。In a specific embodiment, the association indicator matrix H ij distinguishes observed associations from unobserved associations, but only constrains observed associations, so that it is likely to be left at random to some extent A large number of unobserved associations are left unconstrained, resulting in a large amount of noise in the target association matrix.

为了避免前述问题,本公开实施例中,还会构建未知噪声关联约束函数,并基于未知噪声关联约束函数构建最小化目标函数。In order to avoid the aforementioned problems, in the embodiment of the present disclosure, an unknown noise correlation constraint function is also constructed, and a minimization objective function is constructed based on the unknown noise correlation constraint function.

本公开实施例中,未知噪声关联约束函数为

Figure BDA0003317325330000121
其中
Figure BDA0003317325330000122
是对于未知噪声关联的约束,γ被用来控制该项的复杂度。In the embodiment of the present disclosure, the unknown noise correlation constraint function is
Figure BDA0003317325330000121
in
Figure BDA0003317325330000122
is the constraint on the unknown noise association, and γ is used to control the complexity of the term.

应当注意的是,前述的步骤S304和S305可以顺序执行,也可以并行执行。It should be noted that the aforementioned steps S304 and S305 may be performed sequentially, or may be performed in parallel.

步骤S306:基于异质特征关联函数、属性异质网络函数、第一约束函数、第二约束函数和未知噪声关联约束函数,构建最小化目标函数。Step S306: Construct a minimization objective function based on the heterogeneous feature correlation function, the attribute heterogeneous network function, the first constraint function, the second constraint function, and the unknown noise correlation constraint function.

将确定未知噪声关联约束函数后,本公开实施例中构建的最小化目标函数为After the unknown noise correlation constraint function is determined, the minimized objective function constructed in the embodiment of the present disclosure is:

Figure BDA0003317325330000123
Figure BDA0003317325330000123

步骤S307:基于最小化目标函数计算各种类型对象的低秩特征矩阵。Step S307: Calculate low-rank feature matrices of various types of objects based on the minimization objective function.

步骤S308:采用低秩特征矩阵重构对象数据集中各种类型对象的对象特征矩阵。Step S308: Reconstructing object feature matrices of various types of objects in the object dataset by using the low-rank feature matrix.

前述步骤S307-S308与前文实施例步骤S206-S207步骤相同,具体可以参见前文实施例,此处不再复述。The foregoing steps S307-S308 are the same as the steps S206-S207 in the foregoing embodiment. For details, refer to the foregoing embodiment, which will not be repeated here.

前文提及,在本方案实施过程中,可以采用交替方向乘子法对最小化目标函数进行求解,确定各种类型对象的低秩特征矩阵。具体的,采用交替方向乘子法计算各种类型对象的低秩特征矩阵,为采用交替方向乘子法依次交替求解最小化目标函数中的第一权重矩阵,第二权重矩阵,低秩特征矩阵,异质关联网络矩阵,以及各个多层属性异质网络矩阵的基矩阵,直至达到预设的迭代次数或者收敛。As mentioned above, in the implementation process of this solution, the alternating direction multiplier method can be used to solve the minimization objective function, and the low-rank feature matrix of various types of objects can be determined. Specifically, the alternating-direction multiplier method is used to calculate the low-rank feature matrices of various types of objects, and the alternating-direction multiplier method is used to alternately solve the first weight matrix, the second weight matrix, and the low-rank feature matrix in the minimized objective function. , the heterogeneous association network matrix, and the basis matrix of each multi-layer attribute heterogeneous network matrix, until the preset number of iterations or convergence is reached.

本公开实施例中,采用交替方向乘子法对最小化目标函数进行求解可以包括步骤S401-S405。以下以求解公式1.5为例,对本公开实施例采用的交替方向乘子算法进行分析。In the embodiment of the present disclosure, using the alternating direction multiplier method to solve the minimization objective function may include steps S401-S405. The following takes solving formula 1.5 as an example to analyze the alternating direction multiplier algorithm adopted in the embodiment of the present disclosure.

在对公式1.5进行计算前,首先引入约束Gi≥0的拉格朗日乘数

Figure BDA0003317325330000131
因此公式1.5可以等价为公式1.6。Before calculating Equation 1.5, first introduce the Lagrangian multiplier with constraint G i ≥ 0
Figure BDA0003317325330000131
So Equation 1.5 can be equivalent to Equation 1.6.

Figure BDA0003317325330000132
Figure BDA0003317325330000132

随后可以基于公式1.6采用交替方向乘子算法执行步骤S401-S405。Steps S401-S405 may then be performed using an alternating direction multiplier algorithm based on Equation 1.6.

步骤S401:设置第一权重矩阵、第二权重矩阵、低秩特征矩阵和异质关联网络矩阵为固定值,对多层属性异质网络矩阵的基矩阵求第一偏导,在第一偏导值为零时,确定多层属性异质网络矩阵的基矩阵。Step S401: Set the first weight matrix, the second weight matrix, the low-rank feature matrix, and the heterogeneous correlation network matrix as fixed values, and obtain the first partial derivative of the basis matrix of the multi-layer attribute heterogeneous network matrix. When the value is zero, determines the basis matrix of the multi-layer attribute heterogeneous network matrix.

具体的,假设G,S,ωrh已知,可以优化Uit,因此对公式1.6求关于Uit的偏导数:Specifically, assuming that G, S, ω r , ω h are known, U it can be optimized, so the partial derivative with respect to U it is calculated from Equation 1.6:

Figure BDA0003317325330000133
Figure BDA0003317325330000133

对于

Figure BDA0003317325330000134
使得
Figure BDA0003317325330000135
可以得到
Figure BDA0003317325330000136
即多层属性异质网络矩阵的基矩阵为
Figure BDA0003317325330000137
for
Figure BDA0003317325330000134
make
Figure BDA0003317325330000135
can get
Figure BDA0003317325330000136
That is, the basis matrix of the multi-layer attribute heterogeneous network matrix is
Figure BDA0003317325330000137

步骤S402:设置第一权重矩阵、第二权重矩阵、低秩特征矩阵和多层属性异质网络矩阵的基矩阵为固定值,对异质关联网络矩阵求第二偏导,在第二偏导值为零时,确定异质关联网络矩阵。Step S402: Set the basis matrix of the first weight matrix, the second weight matrix, the low-rank feature matrix and the multi-layer attribute heterogeneous network matrix as a fixed value, and obtain the second partial derivative for the heterogeneous correlation network matrix, and in the second partial derivative When the value is zero, the heterogeneous association network matrix is determined.

具体的,假设G,U,ωrh已知,可以优化Sij,因此对式1.6求关于Sij的偏导数得到:Specifically, assuming that G, U, ω r , ω h are known, S ij can be optimized, so the partial derivative with respect to S ij is obtained from equation 1.6:

Figure BDA0003317325330000141
Figure BDA0003317325330000141

对于

Figure BDA0003317325330000142
使得
Figure BDA0003317325330000143
可以得到:
Figure BDA0003317325330000144
即异质关联网络矩阵为
Figure BDA0003317325330000145
for
Figure BDA0003317325330000142
make
Figure BDA0003317325330000143
You can get:
Figure BDA0003317325330000144
That is, the heterogeneous correlation network matrix is
Figure BDA0003317325330000145

步骤S403:设置第一权重矩阵、第二权重矩阵、异质关联网络矩阵和多层属性异质网络矩阵的基矩阵为固定值,对低秩特征矩阵求第三偏导,在第三偏导值为零时,确定低秩特征矩阵。Step S403: Set the basis matrix of the first weight matrix, the second weight matrix, the heterogeneous association network matrix and the multi-layer attribute heterogeneous network matrix as a fixed value, and obtain the third partial derivative for the low-rank feature matrix. When the value is zero, a low-rank feature matrix is determined.

具体的,假设S,U,ωrh已知,可以对式1.6求关于Gi的偏导数:Specifically, assuming that S, U, ω r , ω h are known, the partial derivative with respect to G i can be obtained from Equation 1.6:

Figure BDA0003317325330000146
Figure BDA0003317325330000146

多项式因子λi可以通过令

Figure BDA0003317325330000147
获得,由Karush-Kuhn-Tucker(KKT)条件可得:The polynomial factor λ i can be obtained by making
Figure BDA0003317325330000147
can be obtained from the Karush-Kuhn-Tucker (KKT) condition:

Figure BDA0003317325330000148
Figure BDA0003317325330000148

其中e代表哈达玛积,公式1.10是一个定点方程且解必须满足收敛条件,因此可使得:where e represents the Hadamard product, Equation 1.10 is a fixed-point equation and the solution must satisfy the convergence conditions, so that:

Figure BDA0003317325330000151
Figure BDA0003317325330000151

对于

Figure BDA0003317325330000152
for
Figure BDA0003317325330000152

Figure BDA0003317325330000153
Figure BDA0003317325330000153

对于t=1,2,K,maxitiFor t=1,2,K,max i ti :

Figure BDA0003317325330000154
Figure BDA0003317325330000154

公式1.11、1.12和1.13中的正值和负值符号可分别定义为

Figure BDA0003317325330000155
Figure BDA0003317325330000156
因此,低秩特征矩阵G可更新为:
Figure BDA0003317325330000157
The positive and negative signs in equations 1.11, 1.12 and 1.13 can be defined as
Figure BDA0003317325330000155
and
Figure BDA0003317325330000156
Therefore, the low-rank feature matrix G can be updated as:
Figure BDA0003317325330000157

步骤S404:设置第一权重矩阵、低秩特征矩阵、异质关联网络矩阵和多层属性异质网络矩阵的基矩阵为固定值,对第二权重矩阵求第四偏导,在第四偏导值为零时,确定第二权重矩阵。Step S404: Set the basis matrix of the first weight matrix, the low-rank feature matrix, the heterogeneous association network matrix and the multi-layer attribute heterogeneous network matrix as a fixed value, and obtain the fourth partial derivative for the second weight matrix. When the value is zero, the second weight matrix is determined.

具体的,在对ωh求偏导时,公式1.6右侧的第1、3、5部分与ωh无关,因此可以忽略。进而可得:Specifically, when calculating the partial derivative of ω h , the first, third, and fifth parts on the right side of Equation 1.6 have nothing to do with ω h , so they can be ignored. And thus get:

Figure BDA0003317325330000158
Figure BDA0003317325330000158

Figure BDA0003317325330000159
代表属性异质网络Xit的重构损失,前述公式可以化简为:make
Figure BDA0003317325330000159
Representing the reconstruction loss of the attribute heterogeneous network X it , the aforementioned formula can be simplified as:

Figure BDA00033173253300001510
Figure BDA00033173253300001510

对于公式1.15的求解可以看做是关于vec(ωh)的二次规划问题,可以基于选择性矩阵分解(Selective Non-Matrix Factorization,SNMF)的算法引入拉格朗日乘数来进行求解。The solution of formula 1.15 can be regarded as a quadratic programming problem about vec(ω h ), which can be solved by introducing Lagrangian multipliers based on the Selective Non-Matrix Factorization (SNMF) algorithm.

步骤S405:设置第二权重矩阵、低秩特征矩阵、异质关联网络矩阵和多层属性异质网络矩阵的基矩阵为固定值,对第一权重矩阵求第五偏导,在第五偏导值为零时,确定第一权重矩阵。Step S405: Set the basis matrix of the second weight matrix, the low-rank feature matrix, the heterogeneous association network matrix, and the multi-layer attribute heterogeneous network matrix as a fixed value, obtain the fifth partial derivative for the first weight matrix, and obtain the fifth partial derivative at the fifth partial derivative. When the value is zero, the first weight matrix is determined.

具体的,当对ωr求偏导时,公式1.6右侧的第2、4、5部分与ωr无关,因此可以忽略。进而可得:Specifically, when the partial derivative of ω r is obtained, the parts 2, 4, and 5 on the right side of Equation 1.6 have nothing to do with ω r , so they can be ignored. And thus get:

Figure BDA0003317325330000161
Figure BDA0003317325330000161

Figure BDA0003317325330000162
代表异质关联Rij的重构损失,公式1.16可简化为:make
Figure BDA0003317325330000162
Representing the reconstruction loss of heterogeneous correlation R ij , Equation 1.16 can be simplified to:

Figure BDA0003317325330000163
Figure BDA0003317325330000163

公式1.17可以看做关于vec(ωr)的二次规划问题,也可以基于选择性矩阵分解的算法引入拉格朗日乘数来进行求解。应当注意的是,前述的步骤S401-S405具体实施例中并没有顺序地限制。Equation 1.17 can be regarded as a quadratic programming problem about vec(ω r ), and it can also be solved by introducing Lagrange multipliers based on the algorithm of selective matrix decomposition. It should be noted that the foregoing specific embodiments of steps S401-S405 are not limited in order.

在上述实施例的基础上,本公开实施例提供一种对象确定装置。图4是本公开实施例提供的一种对象确定装置的结构示意图。如图4所示,本公开实施例提供的对象确定装置包括矩阵获取单元401、函数构建单元402、求解单元403和对象特征矩阵确定单元404。On the basis of the foregoing embodiments, the embodiments of the present disclosure provide an object determination apparatus. FIG. 4 is a schematic structural diagram of an object determination apparatus provided by an embodiment of the present disclosure. As shown in FIG. 4 , the object determination apparatus provided by the embodiment of the present disclosure includes a matrix acquisition unit 401 , a function construction unit 402 , a solution unit 403 , and an object feature matrix determination unit 404 .

矩阵获取单元401用于获取对象数据集中各种类型对象的异质特征关联矩阵和对应的关联关系指示矩阵,以及各种类型对象的多层属性异质网络矩阵;The matrix acquisition unit 401 is configured to acquire heterogeneous feature correlation matrices of various types of objects in the object data set and corresponding correlation relationship indication matrices, as well as multi-layer attribute heterogeneous network matrices of various types of objects;

函数构建单元402用于基于异质特征关联矩阵和对应的关联关系指示矩阵构建异质特征关联函数;基于多层属性异质网络矩阵构建属性异质网络函数;以及,基于异质特征关联函数和属性异质网络函数,构建最小化目标函数;The function construction unit 402 is configured to construct a heterogeneous feature correlation function based on the heterogeneous feature correlation matrix and the corresponding correlation indication matrix; construct an attribute heterogeneous network function based on the multi-layer attribute heterogeneous network matrix; and, based on the heterogeneous feature correlation function and Attribute heterogeneity network function to construct the minimization objective function;

求解单元403用于基于最小化目标函数计算各种类型对象的低秩特征矩阵;The solving unit 403 is configured to calculate low-rank feature matrices of various types of objects based on the minimization objective function;

对象特征矩阵确定单元404用于采用低秩特征矩阵重构对象数据集中各种类型对象的对象特征矩阵。The object feature matrix determining unit 404 is configured to reconstruct object feature matrices of various types of objects in the object dataset by using the low-rank feature matrix.

在本公开实施例提供的对象特征矩阵确定装置,通过异质特征关联矩阵和对应的关联关系指示矩阵构建异质特征关联函数,以实现利用关联关系指示矩阵对异质的异质关联和潜在的异质关联进行区分。通过采用各种类型对象对应的低秩特征矩阵和各种类型对象的属性异质网络分解后的基矩阵构建属性异质网络函数,并基于异质特征关联函数和属性异质网络函数构建最小化目标函数,利用最小化目标函数对异质特征关联函数和属性异质网络函数中的第一权重矩阵、低秩特征矩阵、异质关联网络矩阵、第二权重矩阵和各种类型对象的属性异质网络分解后的基矩阵协同分级,而获得各种类型对象的低秩特征矩阵,并基于各种类型对象的低秩特征矩阵确定各种类型对象的对象特征矩阵。In the device for determining the object feature matrix provided by the embodiment of the present disclosure, a heterogeneous feature correlation function is constructed by using the heterogeneous feature correlation matrix and the corresponding correlation relationship indication matrix, so as to realize the heterogeneous correlation and potential correlation between the heterogeneous features using the correlation relationship indicator matrix. Heterogeneous associations are distinguished. The attribute heterogeneous network function is constructed by using the low-rank feature matrix corresponding to various types of objects and the decomposed basis matrix of the attribute heterogeneous network of various types of objects. The objective function is to use the objective function to minimize the first weight matrix, low-rank feature matrix, heterogeneous correlation network matrix, second weight matrix and the attribute difference of various types of objects in the heterogeneous feature correlation function and the attribute heterogeneous network function. The basis matrices decomposed by the qualitative network are co-graded to obtain low-rank feature matrices of various types of objects, and the object feature matrices of various types of objects are determined based on the low-rank feature matrices of various types of objects.

采用本公开实施例提供的装置,可以使得网络的拓扑结构、节点的属性信息融合在一起,从而弥补已知关联不足造成的冷启动问题,进而降低了属性异质网络因同质转换而造成信息损失。By using the device provided by the embodiment of the present disclosure, the topology structure of the network and the attribute information of the nodes can be fused together, so as to make up for the cold start problem caused by the lack of known associations, thereby reducing the information caused by the homogeneous transformation of the heterogeneous network with attributes. loss.

在本公开一些实施例中,函数构建单元402还用于基于异质特征关联函数中的第一权重矩阵构建第一约束函数,以及基于属性异质网络函数中的第二权重矩阵构建第二约束函数。对应的,函数构建单元402基于异质特征关联函数、属性异质网络函数、第一约束函数和第二约束函数,构建最小化目标函数。In some embodiments of the present disclosure, the function constructing unit 402 is further configured to construct a first constraint function based on the first weight matrix in the heterogeneous feature correlation function, and construct a second constraint based on the second weight matrix in the attribute heterogeneous network function function. Correspondingly, the function constructing unit 402 constructs the minimization objective function based on the heterogeneous feature correlation function, the attribute heterogeneous network function, the first constraint function and the second constraint function.

在本公开的一些实施例中,函数构建单元402还用于构建未知噪声关联约束函数。对应的,函数构建单元402基于异质特征关联函数、属性异质网络函数、第一约束函数、第二约束函数和未知噪声关联约束函数,构建最小化目标函数。In some embodiments of the present disclosure, the function construction unit 402 is further configured to construct an unknown noise correlation constraint function. Correspondingly, the function construction unit 402 constructs the minimization objective function based on the heterogeneous feature correlation function, the attribute heterogeneous network function, the first constraint function, the second constraint function and the unknown noise correlation constraint function.

在本公开一些实施例中,求解单元403采用交替方向乘子法对最小化目标函数进行求解,确定各种类型对象的低秩特征矩阵。In some embodiments of the present disclosure, the solving unit 403 uses the alternating direction multiplier method to solve the minimization objective function, and determines low-rank feature matrices of various types of objects.

具体的,在本公开的一些实施例中,求解单元403采用交替方向乘子法依次交替求解最小化目标函数中的第一权重矩阵,第二权重矩阵,低秩特征矩阵,异质关联网络矩阵,以及各个多层属性异质网络矩阵的基矩阵,直至达到预设的迭代次数或者收敛。Specifically, in some embodiments of the present disclosure, the solving unit 403 adopts the alternate direction multiplier method to alternately solve the first weight matrix, the second weight matrix, the low-rank feature matrix, and the heterogeneous correlation network matrix in the minimization objective function in turn. , and the basis matrix of each multi-layer attribute heterogeneous network matrix until the preset number of iterations or convergence is reached.

实际实施中,求解单元403通过设置第一权重矩阵、第二权重矩阵、低秩特征矩阵和异质关联网络矩阵为固定值,对多层属性异质网络矩阵的基矩阵求第一偏导,在第一偏导值为零时,确定多层属性异质网络矩阵的基矩阵;In actual implementation, the solving unit 403 obtains the first partial derivative for the basis matrix of the multi-layer attribute heterogeneous network matrix by setting the first weight matrix, the second weight matrix, the low-rank feature matrix and the heterogeneous correlation network matrix as fixed values, When the first partial derivative value is zero, determine the basis matrix of the multi-layer attribute heterogeneous network matrix;

实际实施中,求解单元403通过设置第一权重矩阵、第二权重矩阵、低秩特征矩阵和多层属性异质网络矩阵的基矩阵为固定值,对异质关联网络矩阵求第二偏导,在第二偏导值为零时,确定异质关联网络矩阵;In actual implementation, the solving unit 403 obtains the second partial derivative for the heterogeneous association network matrix by setting the basis matrix of the first weight matrix, the second weight matrix, the low-rank feature matrix and the multi-layer attribute heterogeneous network matrix as a fixed value, When the second partial derivative value is zero, determine the heterogeneous correlation network matrix;

实际实施中,求解单元403通过设置第一权重矩阵、第二权重矩阵、异质关联网络矩阵和多层属性异质网络矩阵的基矩阵为固定值,对低秩特征矩阵求第三偏导,在第三偏导值为零时,确定低秩特征矩阵;In actual implementation, the solving unit 403 obtains the third partial derivative for the low-rank feature matrix by setting the basis matrix of the first weight matrix, the second weight matrix, the heterogeneous association network matrix and the multi-layer attribute heterogeneous network matrix as a fixed value, When the third partial derivative value is zero, determine the low-rank feature matrix;

实际实施中,求解单元403通过设置第一权重矩阵、低秩特征矩阵、异质关联网络矩阵和多层属性异质网络矩阵的基矩阵为固定值,对第二权重矩阵求第四偏导,在第四偏导值为零时,确定第二权重矩阵;In actual implementation, the solving unit 403 obtains the fourth partial derivative for the second weight matrix by setting the basis matrix of the first weight matrix, the low-rank feature matrix, the heterogeneous association network matrix and the multi-layer attribute heterogeneous network matrix as a fixed value, When the fourth partial derivative value is zero, determine the second weight matrix;

实际实施中,求解单元403通过设置第二权重矩阵、低秩特征矩阵、异质关联网络矩阵和多层属性异质网络矩阵的基矩阵为固定值,对第一权重矩阵求第五偏导,在第五偏导值为零时,确定第一权重矩阵。In actual implementation, the solving unit 403 obtains the fifth partial derivative for the first weight matrix by setting the basis matrix of the second weight matrix, the low-rank feature matrix, the heterogeneous association network matrix and the multi-layer attribute heterogeneous network matrix as a fixed value, When the fifth partial derivative value is zero, the first weight matrix is determined.

本公开实施例中,对象数据集可以为图像数据集,对象数据集中的对象为图像。In this embodiment of the present disclosure, the object dataset may be an image dataset, and the objects in the object dataset are images.

本发明实施例所提供的装置可执行本发明任意实施例所提供的方法,具备执行方法相应的功能模块和有益效果。The apparatus provided by the embodiment of the present invention can execute the method provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method.

图5是本公开实施例提供的一种电子设备的结构示意图,如图5所示,该电子设备包括处理器501、存储器502、输入装置503和输出装置504;电子设备中处理器501的数量可以是一个或多个,图5中以一个处理器501为例;电子设备中的处理器501、存储器502、输入装置503和输出装置504可以通过总线或其他方式连接,图5中以通过总线连接为例。FIG. 5 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure. As shown in FIG. 5 , the electronic device includes a processor 501, a memory 502, an input device 503, and an output device 504; the number of processors 501 in the electronic device There may be one or more, and a processor 501 is taken as an example in FIG. 5; the processor 501, the memory 502, the input device 503, and the output device 504 in the electronic device can be connected by a bus or in other ways. Connect as an example.

存储器502作为一种计算机可读存储介质,可用于存储软件程序、计算机可执行程序以及模块,如本发明实施例中的方法对应的程序指令/模块。处理器501通过运行存储在存储器502中的软件程序、指令以及模块,从而执行电子设备的各种功能应用以及数据处理,即实现本发明实施例所提供的方法。As a computer-readable storage medium, the memory 502 may be used to store software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the methods in the embodiments of the present invention. The processor 501 executes various functional applications and data processing of the electronic device by running the software programs, instructions and modules stored in the memory 502, that is, implements the methods provided by the embodiments of the present invention.

存储器502可主要包括存储程序区和存储数据区,其中,存储程序区可存储操作系统、至少一个功能所需的应用程序;存储数据区可存储根据终端的使用所创建的数据等。此外,存储器502可以包括高速随机存取存储器,还可以包括非易失性存储器,例如至少一个磁盘存储器件、闪存器件、或其他非易失性固态存储器件。在一些实例中,存储器502可进一步包括相对于处理器501远程设置的存储器,这些远程存储器可以通过网络连接至计算机设备。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。The memory 502 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Additionally, memory 502 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, memory 502 may further include memory located remotely from processor 501, which may be connected to the computer device through a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

输入装置503可用于接收输入的数字或字符信息,以及产生与电子设备的用户设置以及功能控制有关的键信号输入,可以包括键盘、鼠标等,输出装置504可包括显示屏等显示设备。The input device 503 can be used to receive input digital or character information, and generate key signal input related to user settings and function control of the electronic device, which can include a keyboard, a mouse, etc., and the output device 504 can include a display device such as a display screen.

本公开实施例还提供了一种包含计算机可执行指令的存储介质,计算机可执行指令在由计算机处理器执行时用于实现本发明实施例所提供的方法。Embodiments of the present disclosure further provide a storage medium containing computer-executable instructions, and the computer-executable instructions, when executed by a computer processor, are used to implement the methods provided by the embodiments of the present disclosure.

当然,本发明实施例所提供的一种包含计算机可执行指令的存储介质,其计算机可执行指令不限于如上的方法操作,还可以执行本发明任意实施例所提供的方法中的相关操作。Certainly, a storage medium containing computer-executable instructions provided by an embodiment of the present invention, the computer-executable instructions of which are not limited to the above method operations, and can also perform related operations in the methods provided by any embodiment of the present invention.

需要说明的是,在本文中,诸如“第一”和“第二”等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来,而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且,术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含,从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素,而且还包括没有明确列出的其他要素,或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下,由语句“包括一个……”限定的要素,并不排除在包括要素的过程、方法、物品或者设备中还存在另外的相同要素。It should be noted that, in this document, relational terms such as "first" and "second" etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply these There is no such actual relationship or sequence between entities or operations. Moreover, the terms "comprising", "comprising" or any other variation thereof are intended to encompass non-exclusive inclusion such that a process, method, article or device comprising a list of elements includes not only those elements, but also includes not explicitly listed or other elements inherent to such a process, method, article or apparatus. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in the process, method, article, or device that includes the element.

以上仅是本公开的具体实施方式,使本领域技术人员能够理解或实现本公开。对这些实施例的多种修改对本领域的技术人员来说将是显而易见的,本文中所定义的一般原理可以在不脱离本公开的精神或范围的情况下,在其它实施例中实现。因此,本公开将不会被限制于本文的这些实施例,而是要符合与本文所公开的原理和新颖特点相一致的最宽的范围。The above are only specific embodiments of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure is not to be limited to the embodiments herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (10)

1. An object feature matrix determination method, comprising:
acquiring heterogeneous characteristic incidence matrixes and corresponding incidence relation indication matrixes of various types of objects in an object data set and multilayer attribute heterogeneous network matrixes of various types of objects;
constructing a heterogeneous characteristic correlation function based on the heterogeneous characteristic correlation matrix and the corresponding incidence relation indication matrix;
constructing an attribute heterogeneous network function based on the multilayer attribute heterogeneous network matrix;
constructing a minimized objective function based on the heterogeneous characteristic correlation function and the attribute heterogeneous network function;
calculating low-rank feature matrices of various types of objects based on the minimized objective function;
and reconstructing an object feature matrix of each type of object in the object data set by using the low-rank feature matrix.
2. The method of claim 1, further comprising:
constructing a first constraint function based on a first weight matrix in the heterogeneous characteristic correlation function;
constructing a second constraint function based on a second weight matrix in the attribute heterogeneous network function;
constructing a minimization objective function based on the heterogeneous characteristic correlation function and the attribute heterogeneous network function, wherein the minimization objective function comprises the following steps:
constructing the minimized objective function based on the heterogeneous feature correlation function, the attribute heterogeneous network function, the first constraint function and the second constraint function.
3. The method of claim 2, further comprising: constructing an unknown noise correlation constraint function;
constructing the minimization objective function based on the heterogeneous feature correlation function, the attribute heterogeneous network function, the first constraint function and the second constraint function, including:
and constructing the minimized objective function based on the heterogeneous characteristic correlation function, the attribute heterogeneous network function, the first constraint function, the second constraint function and the unknown noise correlation constraint function.
4. The method according to any of claims 1-3, wherein said determining a low rank feature matrix for each type of object based on said minimization objective function comprises:
and solving the minimized objective function by adopting an alternating direction multiplier method, and determining low-rank characteristic matrixes of various types of objects.
5. The method of claim 4, wherein solving the minimized objective function using an alternating direction multiplier method comprises:
and adopting the alternating direction multiplier method to sequentially and alternately solve the first weight matrix, the second weight matrix, the low-rank characteristic matrix, the heterogeneous correlation network matrix and the base matrix of each multilayer attribute heterogeneous network matrix in the minimized objective function until the preset iteration times or convergence is reached.
6. The method of claim 5, wherein the alternating direction multiplier method is adopted to sequentially and alternately solve the first weight matrix, the second weight matrix, the low rank feature matrix, the heterogeneous correlation network matrix and the base matrix of the multi-layer property heterogeneous network matrix in the minimized objective function, and comprises:
setting the first weight matrix, the second weight matrix, the low-rank feature matrix and the heterogeneous correlation network matrix as fixed values, solving a first partial derivative of a base matrix of the multilayer attribute heterogeneous network matrix, and determining the base matrix of the multilayer attribute heterogeneous network matrix when the first partial derivative value is zero;
setting the first weight matrix, the second weight matrix, the low-rank characteristic matrix and the base matrix of the multi-layer attribute heterogeneous network matrix as fixed values, solving a second partial derivative of the heterogeneous correlation network matrix, and determining the heterogeneous correlation network matrix when the second partial derivative value is zero;
setting the first weight matrix, the second weight matrix, the heterogeneous correlation network matrix and the base matrix of the multilayer attribute heterogeneous network matrix as fixed values, solving a third partial derivative of the low-rank feature matrix, and determining the low-rank feature matrix when the third partial derivative value is zero;
setting the first weight matrix, the low-rank feature matrix, the heterogeneous correlation network matrix and the base matrix of the multilayer attribute heterogeneous network matrix as fixed values, solving a fourth partial derivative of the second weight matrix, and determining the second weight matrix when the fourth partial derivative value is zero;
setting the second weight matrix, the low-rank feature matrix, the heterogeneous correlation network matrix and the base matrix of the multilayer attribute heterogeneous network matrix as fixed values, solving a fifth partial derivative of the first weight matrix, and determining the first weight matrix when the fifth partial derivative value is zero.
7. The method according to any of claims 1-3, wherein the object data set is an image data set and the object in the object data set is an image.
8. An object feature matrix determination apparatus, comprising:
the matrix acquisition unit is used for acquiring a heterogeneous characteristic incidence matrix and a corresponding incidence relation indication matrix of various types of objects in the object data set and a multilayer attribute heterogeneous network matrix of various types of objects;
the function construction unit is used for constructing a heterogeneous characteristic incidence function based on the heterogeneous characteristic incidence matrix and the corresponding incidence relation indication matrix; constructing an attribute heterogeneous network function based on the multilayer attribute heterogeneous network matrix; constructing a minimized objective function based on the heterogeneous characteristic correlation function and the attribute heterogeneous network function;
the solving unit is used for calculating low-rank characteristic matrixes of various types of objects based on the minimized objective function;
and the object feature matrix determining unit is used for reconstructing the object feature matrix of each type of object in the object data set by adopting the low-rank feature matrix.
9. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement a method as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the method of any one of claims 1 to 7.
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