CN114742132A - Deep multi-view clustering method, system and equipment based on common difference learning - Google Patents

Deep multi-view clustering method, system and equipment based on common difference learning Download PDF

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CN114742132A
CN114742132A CN202210264054.3A CN202210264054A CN114742132A CN 114742132 A CN114742132 A CN 114742132A CN 202210264054 A CN202210264054 A CN 202210264054A CN 114742132 A CN114742132 A CN 114742132A
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李晓翠
张新玉
史庆宇
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Hunan University of Technology
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Abstract

The embodiment of the disclosure provides a deep multi-view clustering method, system and device based on common difference learning, belonging to the technical field of data processing, and specifically comprising the following steps: establishing a common difference depth multi-view feature learning network; respectively connecting each view of the multi-view data with a common information extraction network and a difference information extraction network; inputting the common information extraction network of all views of the multi-view data into a common information learning module for training until convergence; inputting a common information extraction network and a difference information extraction network of all views of the multi-view data into a difference information learning module, and obtaining the complementary characteristics of each view of the multi-view data through orthogonal constraint; connecting the consistency features and all the complementary features in series to form a multi-view fusion feature; and inputting the multi-view fusion characteristics into a clustering model based on KL divergence for clustering. By the scheme, the clustering effect and the adaptability of the multi-view data under the condition of initial severe imbalance are improved.

Description

Deep multi-view clustering method, system and equipment based on common difference learning
Technical Field
The embodiment of the disclosure relates to the technical field of data processing, in particular to a deep multi-view clustering method, system and device based on common difference learning.
Background
At present, the basic idea of clustering is to divide samples in a data set into a plurality of clusters according to the similarity between the samples, and the similarity between the samples of the same class is smaller than the similarity between the samples of different classes. The traditional clustering algorithm mainly aims at single-view data, and the data only has one group of characteristics. When the data has multiple sets of features, it is referred to as multi-view data. Not only does multi-view data contain more rich and useful information, but redundant information is brought between different views. Most of multi-view clustering at present mainly focuses on maximizing the common information of multiple views, and ignores the difference information on each view, namely complementary information of multi-view data is not fully mined; in the case of initial extreme imbalance of multi-view data, the existing method may generate a "barrel effect", that is, common information of all views may get closer to the view with the worst initial characteristic, and the characteristics of high-quality views are not fully utilized, which also loses the meaning of data described from multiple views.
Therefore, a deep multi-view clustering method based on common difference learning with high clustering effect and adaptability is urgently needed.
Disclosure of Invention
In view of this, the embodiments of the present disclosure provide a deep multi-view clustering method, system and device based on common difference learning, which at least partially solve the problem in the prior art that the clustering effect and the adaptability using high-quality view features are poor.
In a first aspect, an embodiment of the present disclosure provides a deep multi-view clustering method based on common difference learning, including:
step 1, establishing a common difference depth multi-view feature learning network, wherein the common difference depth multi-view feature learning network comprises a depth feature extraction module, a common information learning module and a difference information learning module, and the depth feature extraction module comprises a common information extraction network and a difference information extraction network;
step 2, obtaining multi-view data, and respectively connecting each view of the multi-view data with the common information extraction network and the difference information extraction network;
step 3, inputting the common information of all views of the multi-view data into a common information learning module for training until convergence, and obtaining the consistency characteristic of the multi-view data;
step 4, inputting the common information extraction network and the difference information extraction network of all the views of the multi-view data into a difference information learning module, and obtaining the complementary characteristics of each view of the multi-view data through orthogonal constraint;
step 5, connecting the consistency features and all the complementary features in series to form a multi-view fusion feature;
and 6, inputting the multi-view fusion characteristics into a KL divergence-based clustering model for clustering.
According to a specific implementation of the embodiment of the present disclosure, the common information learning module includes generating a countermeasure network.
According to a specific implementation manner of the embodiment of the present disclosure, the step 3 specifically includes:
step 3.1, the common information learning module takes a common information extraction network on each view as a generator G to finally obtain M generators;
step 3.2, transmitting the feature data generated by the M generators into a discriminator D of M categories;
and 3.3, repeating the step 3.1 and the step 3.2 until the discriminator cannot distinguish the view corresponding to the characteristic data to obtain the consistency characteristic.
According to a specific implementation manner of the embodiment of the present disclosure, the series connection manner of step 5 is
Figure BDA0003551931290000021
Wherein h isiA multi-view fusion feature representing the ith sample in the mth view,
Figure BDA0003551931290000022
and
Figure BDA0003551931290000023
respectively representing the common information and the disparity information extracted on view m.
According to a specific implementation manner of the embodiment of the present disclosure, the step 6 specifically includes:
inputting the multi-view fusion characteristics into a KL divergence-based clustering model to iteratively train the common difference depth multi-view characteristic learning network and the clustering network, and clustering the multi-view data.
In a second aspect, an embodiment of the present disclosure provides a deep multi-view clustering system based on common difference learning, including:
the system comprises an establishing module, a judging module and a judging module, wherein the establishing module is used for establishing a common difference depth multi-view feature learning network, the common difference depth multi-view feature learning network comprises a depth feature extracting module, a common information learning module and a difference information learning module, and the depth feature extracting module comprises a common information extracting network and a difference information extracting network;
an obtaining module, configured to obtain multi-view data, and connect each view of the multi-view data to the common information extraction network and the difference information extraction network, respectively;
the first learning module is used for inputting the common information extraction network of all the views of the multi-view data into the common information learning module for training until convergence to obtain the consistency characteristic of the multi-view data;
the second learning module is used for inputting the common information extraction network and the difference information extraction network of all the views of the multi-view data into the difference information learning module, and obtaining the complementary characteristics of each view of the multi-view data through orthogonal constraint;
a fusion module for concatenating the consistent features and all of the complementary features to form a multi-view fusion feature;
and the clustering module is used for inputting the multi-view fusion characteristics into a clustering model based on KL divergence for clustering.
In a third aspect, an embodiment of the present disclosure further provides an electronic device, where the electronic device includes:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method for common difference learning based deep multi-view clustering in any implementation of the first aspect or the first aspect.
The depth multi-view clustering scheme based on common difference learning in the embodiment of the disclosure comprises: step 1, establishing a common difference depth multi-view feature learning network, wherein the common difference depth multi-view feature learning network comprises a depth feature extraction module, a common information learning module and a difference information learning module, and the depth feature extraction module comprises a common information extraction network and a difference information extraction network; step 2, obtaining multi-view data, and respectively connecting each view of the multi-view data with the common information extraction network and the difference information extraction network; step 3, inputting the common information of all views of the multi-view data to a common information learning module for training until convergence, and obtaining the consistency characteristics of the multi-view data; step 4, inputting the common information extraction network and the difference information extraction network of all the views of the multi-view data into a difference information learning module, and obtaining the complementary characteristics of each view of the multi-view data through orthogonal constraint; step 5, connecting the consistency features and all the complementary features in series to form a multi-view fusion feature; and 6, inputting the multi-view fusion characteristics into a clustering model based on KL divergence for clustering.
The beneficial effects of the embodiment of the disclosure are: by the scheme, the consistency and complementary information of multi-view data are fully utilized by multi-view feature learning and multi-view clustering strategies, redundant information among different views is reduced, and clustering effect and adaptability are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required to be used in the embodiments will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a deep multi-view clustering method based on common difference learning according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of another deep multi-view clustering method based on common difference learning according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a common disparity depth multi-view feature learning network according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a depth feature extraction module according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a common information learning module according to an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of a difference information learning module according to an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of a deep multi-view clustering system based on common difference learning according to an embodiment of the present disclosure;
fig. 8 is a schematic view of an electronic device provided in an embodiment of the present disclosure.
Detailed Description
The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
The embodiments of the present disclosure are described below with specific examples, and other advantages and effects of the present disclosure will be readily apparent to those skilled in the art from the disclosure in the specification. It is to be understood that the embodiments described are only a few embodiments of the present disclosure, and not all embodiments. The disclosure may be embodied or carried out in various other specific embodiments, and various modifications and changes may be made in the details within the description without departing from the spirit of the disclosure. It should be noted that the features in the following embodiments and examples may be combined with each other without conflict. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
It is noted that various aspects of the embodiments are described below within the scope of the appended claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the disclosure, one skilled in the art should appreciate that one aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. Additionally, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present disclosure, and the drawings only show the components related to the present disclosure rather than the number, shape and size of the components in actual implementation, and the type, amount and ratio of the components in actual implementation may be changed arbitrarily, and the layout of the components may be more complicated.
In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects may be practiced without these specific details.
The embodiment of the disclosure provides a deep multi-view clustering method based on common difference learning, and the method can be applied to a multi-view data clustering analysis process in a data processing scene.
Referring to fig. 1, a schematic flow chart of a deep multi-view clustering method based on common difference learning according to an embodiment of the present disclosure is provided. As shown in fig. 1 and 2, the method mainly comprises the following steps:
step 1, establishing a common difference depth multi-view feature learning network, wherein the common difference depth multi-view feature learning network comprises a depth feature extraction module, a common information learning module and a difference information learning module, and the depth feature extraction module comprises a common information extraction network and a difference information extraction network;
in specific implementation, the common disparity depth multi-view feature learning network may be constructed first, so that the common disparity depth multi-view feature learning network may fully utilize consistency and complementary information of multi-view data, and reduce redundant information between different views, where the structure of the common disparity depth multi-view feature learning network is shown in fig. 3, and meanwhile, the common disparity depth multi-view feature learning network includes the depth feature extraction module shown in fig. 4, the common information learning module shown in fig. 5, and the disparity information learning module shown in fig. 6, where the depth feature extraction module includes a common information extraction network and a disparity information extraction network.
Step 2, obtaining multi-view data, and respectively connecting each view of the multi-view data with the common information extraction network and the difference information extraction network;
for example, let multiview data be X ═ { X ═ X(1),X(2),...,X(M)Where M represents the total number of views of the data,
Figure BDA0003551931290000061
dmrepresenting the characteristic dimension of the sample in the mth view and N representing the total number of samples. Each view of the multi-view data may then be connected separatelyThe common information extraction network and the difference information extraction network output the common information of each view, and the difference information extraction network outputs the difference information of each view.
Specifically, the depth feature extraction model includes: and a common information extraction network and a difference information extraction network for extracting common information of all views and difference information implied in each view.
Let the common information extraction sub-network and the difference information extraction sub-network in each view both contain n +1 full connection layers, the kth e [0, n ∈]Layers respectively containing and pskAnd (4) a unit. The output at the k-th layer of the common information network for sample x in the m-th view can be expressed as:
Figure BDA0003551931290000071
wherein,
Figure BDA0003551931290000072
and
Figure BDA0003551931290000073
and respectively representing the weight matrix and the offset vector of the k-th layer in the common information extraction sub-network.
Figure BDA0003551931290000074
Nonlinear activation functions, commonly used are sigmoid, tanh and the like.
Meanwhile, the output at the k layer of the disparity information network for sample x in the mth view can be expressed as:
Figure BDA0003551931290000075
wherein,
Figure BDA0003551931290000076
and
Figure BDA0003551931290000077
respectively representing the weight matrix and the offset vector of the k-th layer in the difference information extraction sub-network.
Thus, for the ith sample in the mth view
Figure BDA0003551931290000078
The corresponding common information and difference information can be obtained respectively and are recorded as:
Figure BDA0003551931290000079
(common information) and
Figure BDA00035519312900000710
(difference information).
Figure BDA00035519312900000711
Figure BDA00035519312900000712
Step 3, inputting the common information of all views of the multi-view data into a common information learning module for training until convergence, and obtaining the consistency characteristic of the multi-view data;
optionally, the common information learning module includes generating a countermeasure network.
Further, the step 3 specifically includes:
step 3.1, the common information learning module takes a common information extraction network on each view as a generator G to finally obtain M generators;
step 3.2, transmitting the feature data generated by the M generators into a discriminator D of M categories;
and 3.3, repeating the step 3.1 and the step 3.2 until the identifier cannot distinguish the view corresponding to the feature data to obtain the consistency feature.
In specific implementation, the common information learning module takes the depth common information extraction sub-network on each view as a generator G, and finally M generators are obtained. The feature data generated by the M generators are passed to a discriminator D of M classes. The goal of G is to generate data with a similar distribution such that discriminator D cannot distinguish from which view the data came from, while the goal of D is to try to distinguish from which view the incoming feature data came from or was generated by which generator G. Through counterlearning, the common information extracted in each view is finally made similar enough to form the consistency feature. I.e. to maximize the information common in the different views. The module objective function is as follows:
Figure BDA0003551931290000081
wherein, GmRepresenting the generator (common information extraction network) on the mth view,
Figure BDA0003551931290000082
representing a sample
Figure BDA0003551931290000083
Through generator GmThe samples that are generated are then sampled,
Figure BDA0003551931290000084
representing a sample
Figure BDA0003551931290000085
The real view tag.
Figure BDA0003551931290000086
Is the probability value that the generated sample originates from view m, i.e.:
Figure BDA0003551931290000087
step 4, inputting the common information extraction network and the difference information extraction network of all the views of the multi-view data into a difference information learning module, and obtaining the complementary characteristics of each view of the multi-view data through orthogonal constraint;
in specific implementation, the common information extraction network and the difference information extraction network of all the views of the multi-view data can be input into the difference information learning module, and through orthogonal constraint, the correlation between the common information and the difference information is minimized, so that the complementary characteristics of each view are obtained.
Step 5, connecting the consistency features and all the complementary features in series to form a multi-view fusion feature;
on the basis of the above embodiment, the series connection mode of the step 5 is
Figure BDA0003551931290000091
Wherein h isiA multi-view fusion feature representing the ith sample in the mth view,
Figure BDA0003551931290000092
and
Figure BDA0003551931290000093
respectively representing the common information and the disparity information extracted on view m.
In specific implementation, for the ith sample in the mth view
Figure BDA0003551931290000094
By using
Figure BDA0003551931290000095
And
Figure BDA0003551931290000096
respectively representing the common information and the disparity information extracted on view m. Then, the common information vectors and the difference information vectors extracted from all the views are fused in the following way to obtain the common difference information h of the sample iiAs the multi-view fusion feature.
Figure BDA0003551931290000097
Wherein h isc,iInformation common to all views is represented, calculated by the following formula,
Figure BDA0003551931290000098
and 6, inputting the multi-view fusion characteristics into a clustering model based on KL divergence for clustering.
Optionally, the step 6 specifically includes:
inputting the multi-view fusion characteristics into a KL divergence-based clustering model to iteratively train the common difference depth multi-view characteristic learning network and the clustering network, and clustering the multi-view data.
In specific implementation, the common difference characteristics of the multiple views can be input into a KL-based clustering model, a common difference information learning network and a clustering network are iteratively trained, and clustering is performed on the multiple view data. The objective function of the depth multi-view clustering algorithm based on common difference learning is as follows:
L＝Lc+λ1Ls+λ2Lclu (8)
wherein λ is1，λ2For balancing factors, for adjusting the proportion of losses of the parts in the overall objective function, LcluFor clustering loss, it is calculated by the following formula:
Figure BDA0003551931290000101
where K is the number of classes of the cluster, qijSoft assignment probability, p, for samples i belonging to cluster jijIs the target probability that sample i belongs to cluster j.
qijAnd pijRespectively calculated by the following methods:
Figure BDA0003551931290000102
wherein u isjFor the cluster center of the j-th class, α is a degree of freedom variable whose value is fixed to 1 for simplicity of calculation.
Figure BDA0003551931290000103
Wherein f isjSum of soft assignment probabilities for all samples belonging to jth cluster:
Figure BDA0003551931290000104
the depth multi-view clustering method based on common difference learning provided by the embodiment includes two networks through a depth feature extraction submodule, wherein one of the two networks is used for extracting common information, and the other network is used for extracting difference information. The common information learning module is used for enabling the common information extracted from each view to be similar as much as possible by fusing the GAN technology; and the difference information learning module minimizes the correlation between the common information and the difference information through orthogonal constraint.
And then applying the common difference depth multi-view feature learning network to multi-view clustering, transmitting the common difference information extracted by the multi-view feature learning network into a subsequent clustering network, and achieving the purpose of clustering multi-view data by iteratively training the common difference information learning network and the clustering network, thereby improving the clustering effect and the adaptability.
Corresponding to the above method embodiment, referring to fig. 7, the disclosed embodiment further provides a depth multi-view clustering system 70 based on common difference learning, including:
an establishing module 701, configured to establish a common difference depth multi-view feature learning network, where the common difference depth multi-view feature learning network includes a depth feature extracting module, a common information learning module, and a difference information learning module, and the depth feature extracting module includes a common information extracting network and a difference information extracting network;
an obtaining module 702, configured to obtain multi-view data, and connect each view of the multi-view data to the common information extraction network and the difference information extraction network respectively;
a first learning module 703, configured to extract common information of all views of the multi-view data from a network, and input the extracted common information to the common information learning module for training until convergence, so as to obtain a consistency characteristic of the multi-view data;
a second learning module 704, configured to input the common information extraction network and the disparity information extraction network of all views of the multi-view data into the disparity information learning module, and obtain the complementary feature of each view of the multi-view data through orthogonal constraint;
a fusion module 705 for concatenating the identity feature and all the complementary features to form a multi-view fusion feature;
and the clustering module 706 is used for inputting the multi-view fusion features into a clustering model based on KL divergence for clustering.
The system shown in fig. 7 may correspondingly execute the content in the above method embodiment, and details of the part not described in detail in this embodiment refer to the content described in the above method embodiment, which is not described again here.
Referring to fig. 8, an embodiment of the present disclosure also provides an electronic device 80, which includes: at least one processor and a memory communicatively coupled to the at least one processor. Wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the common difference learning based deep multi-view clustering method in the foregoing method embodiments.
The disclosed embodiments also provide a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the common difference learning-based deep multi-view clustering method in the foregoing method embodiments.
The disclosed embodiments also provide a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the common difference learning based deep multi-view clustering method in the aforementioned method embodiments.
Referring now to FIG. 8, a block diagram of an electronic device 80 suitable for use in implementing embodiments of the present disclosure is shown. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 8 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 8, the electronic device 80 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 801 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)802 or a program loaded from a storage means 808 into a Random Access Memory (RAM) 803. In the RAM 803, various programs and data necessary for the operation of the electronic apparatus 80 are also stored. The processing apparatus 801, the ROM 802, and the RAM 803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.
Generally, the following devices may be connected to the I/O interface 805: input devices 806 including, for example, touch screens, touch pads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, and so forth; output devices 807 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; storage 808 including, for example, magnetic tape, hard disk, etc.; and a communication device 809. The communication means 809 may allow the electronic device 80 to communicate wirelessly or by wire with other devices to exchange data. While the figures illustrate an electronic device 80 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, the processes described above with reference to the flow diagrams may be implemented as computer software programs, according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication means 809, or installed from the storage means 808, or installed from the ROM 802. The computer program, when executed by the processing apparatus 801, performs the above-described functions defined in the methods of the embodiments of the present disclosure.
It should be noted that the computer readable medium of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to perform the steps associated with the method embodiments.
Alternatively, the computer readable medium carries one or more programs, which when executed by the electronic device, enable the electronic device to perform the relevant steps of the above method embodiments.
Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as python, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware.
It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof.
The above description is only for the specific embodiments of the present disclosure, but the scope of the present disclosure is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present disclosure should be covered within the scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.

Claims (7)

1. A deep multi-view clustering method based on common difference learning is characterized by comprising the following steps:
step 1, establishing a common difference depth multi-view feature learning network, wherein the common difference depth multi-view feature learning network comprises a depth feature extraction module, a common information learning module and a difference information learning module, and the depth feature extraction module comprises a common information extraction network and a difference information extraction network;
step 2, obtaining multi-view data, and respectively connecting each view of the multi-view data with the common information extraction network and the difference information extraction network;
step 3, inputting the common information of all views of the multi-view data into a common information learning module for training until convergence, and obtaining the consistency characteristic of the multi-view data;
step 4, inputting the common information extraction network and the difference information extraction network of all the views of the multi-view data into a difference information learning module, and obtaining the complementary characteristics of each view of the multi-view data through orthogonal constraint;
step 5, connecting the consistency features and all the complementary features in series to form a multi-view fusion feature;
and 6, inputting the multi-view fusion characteristics into a clustering model based on KL divergence for clustering.
2. The method of claim 1, wherein the common information learning module comprises generating a countermeasure network.
3. The method according to claim 2, wherein the step 3 specifically comprises:
step 3.1, the common information learning module takes a common information extraction network on each view as a generator G to finally obtain M generators;
step 3.2, transmitting the feature data generated by the M generators into a discriminator D of M categories;
and 3.3, repeating the step 3.1 and the step 3.2 until the discriminator cannot distinguish the view corresponding to the characteristic data to obtain the consistency characteristic.
4. The method according to claim 1, wherein the series connection of step 5 is
Figure FDA0003551931280000011
Wherein h isiA multi-view fusion feature representing the ith sample in the mth view,
Figure FDA0003551931280000021
and
Figure FDA0003551931280000022
respectively representing the common information and the disparity information extracted on view m.
5. The method according to claim 1, wherein the step 6 specifically comprises:
inputting the multi-view fusion characteristics into a KL divergence-based clustering model to iteratively train the common difference depth multi-view characteristic learning network and the clustering network, and clustering the multi-view data.
6. A deep multi-view clustering system based on common difference learning, comprising:
the system comprises an establishing module, a judging module and a judging module, wherein the establishing module is used for establishing a common difference depth multi-view feature learning network, the common difference depth multi-view feature learning network comprises a depth feature extracting module, a common information learning module and a difference information learning module, and the depth feature extracting module comprises a common information extracting network and a difference information extracting network;
the acquisition module is used for acquiring multi-view data and respectively connecting each view of the multi-view data with the common information extraction network and the difference information extraction network;
the first learning module is used for inputting the common information extraction network of all the views of the multi-view data into the common information learning module for training until convergence to obtain the consistency characteristic of the multi-view data;
the second learning module is used for inputting the common information extraction network and the difference information extraction network of all the views of the multi-view data into the difference information learning module, and obtaining the complementary characteristics of each view of the multi-view data through orthogonal constraint;
a fusion module for concatenating the consistent features and all of the complementary features to form a multi-view fusion feature;
and the clustering module is used for inputting the multi-view fusion characteristics into a clustering model based on KL divergence for clustering.
7. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the common difference learning based deep multiview clustering method of any of the preceding claims 1-5.
CN202210264054.3A 2022-03-17 2022-03-17 Deep multi-view clustering method, system and equipment based on common difference learning Pending CN114742132A (en)

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