CN108921226A - A kind of zero sample classification method based on low-rank representation and manifold regularization - Google Patents

A kind of zero sample classification method based on low-rank representation and manifold regularization Download PDF

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CN108921226A
CN108921226A CN201810759171.0A CN201810759171A CN108921226A CN 108921226 A CN108921226 A CN 108921226A CN 201810759171 A CN201810759171 A CN 201810759171A CN 108921226 A CN108921226 A CN 108921226A
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class data
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CN108921226B (en
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孟敏
詹箫玉
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Guangdong University of Technology
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Abstract

The zero sample classification method based on low-rank representation and manifold regularization that the invention discloses a kind of, including：Calculate the mapping relations in visible class data set between the visual signature and semantic feature of sample；Calculate the semantic expressiveness of sample in invisible class data set；It introduces sparse constraint and Laplce's canonical is combined to constrain, calculate the low-rank representation of sample in invisible class data set；Calculate weight matrix and Laplacian Matrix；Manifold regularization is introduced, the noise of semantic expressiveness in invisible class data set is removed；It predicts the label of sample in invisible class data set, realizes sample classification.The limitation that the zero sample classification method based on low-rank representation and manifold regularization that the present invention designs effectively overcomes conventional sorting methods low for nicety of grading when sample size is few, sample label information is lost, obtain more accurate semantic expressiveness on invisible class data set, enhance the descriptive power to data characteristics, the precision of zero sample classification can be effectively improved.

Description

A kind of zero sample classification method based on low-rank representation and manifold regularization
Technical field
The present invention relates to sample classification technical field more particularly to a kind of zero samples based on low-rank representation and manifold regularization This classification method.
Background technique
In large-scale classification problem, lack enough training samples, perhaps the label information of multisample is lost, one Determine to limit the raising of nicety of grading in degree.Zero sample classification is a kind of effective solution side proposed for this problem Method.
Usually assume that sample data is all distributed in the structure in the subspace of low-dimensional and with low-rank in the prior art.It is existing Method is based on data distribution approximation across multiple lower-dimensional subspaces it is assumed that being absorbed in the low-rank representation for finding data.It passes through l1/l2Norm handles outlier, and has accurately restored the subspace structure of sample under certain technical conditions, detects simultaneously Outlier is gone out.However when data distribution is when combining nonlinear subspace, such methods can not accurately restore the several of data What structure.In actual application, the face-image of face is exactly to be located in nonlinear manifold structure.
In terms of sample denoising, the prior art usually assumes that sample data is strictly distributed in manifold, however is actually answering In, often all there is noise in sample data.In this case, certain methods pass through the knot in punishment manifold locally or globally Structure handles noise problem, however this excessive punishment would generally reduce the generalization ability of classifier, result in currently scarce When the label information of weary enough training samples or sample is lost, the low problem of nicety of grading.
Summary of the invention
The zero sample classification method based on low-rank representation and manifold regularization that the present invention provides a kind of, solves and currently exists When lacking the label information of enough training samples or sample and losing, the low technical problem of nicety of grading.Provided by the invention one Zero sample classification method of the kind based on low-rank representation and manifold regularization, including：
Step 1：Calculate the visual signature X of sample in visible class data setsWith semantic expressiveness AsBetween mapping relationship f, i.e., f:Xs→As, wherein visible class data set is For the visual signature of sample in visible class data set, p is the dimension of sample visual signature,For visible class The semantic expressiveness of sample in data set, q are the dimension that each sample corresponds to semantic expressiveness, csFor the class of visible class data set sample Not total, m is the total sample number of visible class data set；
Step 2：The semantic expressiveness A of sample in invisible class data set is calculated using mapping relationship fu, wherein invisible class Data set isFor in invisible class data set The visual signature of sample andcuFor the classification sum of invisible class data set sample, n is invisible class data set Total sample number,For the invisible class data set X being calculateduSemantic expressiveness,
Step 3：Calculate the non-negative sparse low-rank representation Z of the Laplace regularization of sample in invisible class data set；
Step 4：Weight matrix W and Laplacian Matrix L is calculated using low-rank representation Z；
Step 5：Manifold regularization is introduced, the noise of the semantic expressiveness in invisible class data set is removed；
Step 6：Using the semantic expressiveness in the invisible class data set after denoising, sample in invisible class data set is predicted Label, realize sample classification.
Preferably, the non-negative sparse low-rank of the Laplace regularization of sample in invisible class data set is calculated in step 3 Indicate Z expression formula be：
s.t.Xu=XuZ+E
Z≥0
||Z||0≤T
Wherein E is noise, and α is the first pre-setting tuning parameters, and β is the second pre-setting tuning parameters, | | | |*Indicate core model Number, | | | |1Indicate l1Norm, tr () indicate trace function, and Z >=0 ensure that the non-negative characteristic of matrix Z, ‖ Z | |0≤ T ensure that The sparse characteristic of matrix Z.
Preferably, manifold regularization is introduced in step 5, removes the public affairs of the noise of the semantic expressiveness in invisible class data set Formula is：
Wherein, I is unit matrix, and λ is third pre-setting tuning parameters,For the semanteme in invisible class data set after denoising It indicates.
It can be seen that the present invention from the above summary of the invention to have the following advantages that：
The present invention passes through low-rank representation and manifold regularization when sample size is few, sample label information is lost More accurate semantic expressiveness on invisible class data set is obtained, enhances the descriptive power to data characteristics, can effectively improve The precision of zero sample classification solves currently when the label information for lacking enough training samples or sample is lost, classification essence Spend low problem.
Detailed description of the invention
Fig. 1 is a kind of zero sample classification method based on low-rank representation and manifold regularization provided in an embodiment of the present invention Flow diagram.
Fig. 2 is a kind of part of the zero sample classification method based on low-rank representation and manifold regularization provided in this embodiment Classification results schematic diagram.
Specific embodiment
Attribute Pascal and Yahoo (aPY) data set includes 32 classifications, wherein 20 classifications are visible Class, for training, 12 classifications are invisible classes, for testing.Each sample has 64 attribute informations.The present embodiment uses APY data set does exemplary illustration to method proposed by the present invention.To enable goal of the invention of the invention, feature, advantage More obvious and understandable, following will be combined with the drawings in the embodiments of the present invention, to the method in the embodiment of the present invention carry out it is clear, It is fully described by, it is clear that the embodiments described below are only a part of the embodiment of the present invention, and not all embodiment. Based on the embodiments of the present invention, obtained by those of ordinary skill in the art without making creative efforts all Other embodiments shall fall within the protection scope of the present invention.
Referring to Fig. 1, a kind of zero sample classification based on low-rank representation and manifold regularization provided in an embodiment of the present invention One embodiment of method, including：
Step 1：Calculate the visual signature X of sample in visible class data setsWith semantic expressiveness AsBetween mapping relationship f, i.e., f:Xs→As, wherein visible class data set is For the visual signature of sample in visible class data set, p is the dimension of sample visual signature,For visible class The semantic expressiveness of sample in data set, q are the dimension that each sample corresponds to semantic expressiveness, csFor the class of visible class data set sample Not total, m is the total sample number of visible class data set；
Step 2：The semantic expressiveness A of sample in invisible class data set is calculated using mapping relationship fu, wherein invisible class Data set isFor in invisible class data set The visual signature of sample andcuFor the classification sum of invisible class data set sample, n is invisible class data set Total sample number,For the invisible class data set X being calculateduSemantic expressiveness,
Step 3：Calculate the non-negative sparse low-rank representation Z of the Laplace regularization of sample in invisible class data set；
It should be noted that introducing sparse constraint in order to preferably obtain the partial structurtes of data, calculating invisible class The expression formula of the non-negative sparse low-rank representation Z of the Laplace regularization of sample is in data set：
s.t.Xu=XuZ+E
Z≥0
||Z||0≤T
Wherein E is noise, and α is the first pre-setting tuning parameters, and β is the second pre-setting tuning parameters, ‖ ‖*Indicate nuclear norm, | |·||1Indicate l1Norm, tr () indicate trace function, and Z >=0 ensure that the non-negative characteristic of matrix Z, ‖ Z ‖0≤ T ensure that matrix Z Sparse characteristic.
Step 4：Weight matrix W and Laplacian Matrix L is calculated using low-rank representation Z；
It should be noted that the formula for calculating weight matrix W is：
Calculate Laplacian Matrix L formula be：
L=D-W (3)
Wherein, D is the degree matrix of n × n, that is, includes element { d1,d2,d3,...,dnDiagonal matrix, k-th is diagonal Element dkIndicate the sum of the weighted value on all sides being connected in undirected weight map with k-th of vertex；
Step 5：Manifold regularization is introduced, the noise of the semantic expressiveness in invisible class data set is removed；
It should be noted that introducing manifold regularization, the public affairs of the noise of the semantic expressiveness in invisible class data set are removed Formula is：
Wherein, I is unit matrix, and λ is third pre-setting tuning parameters,For the semanteme in invisible class data set after denoising It indicates.
Step 6：Using the semantic expressiveness in the invisible class data set after denoising, sample in invisible class data set is predicted Label, realize sample classification, formula is：
Referring to Fig. 2, Fig. 2 is a kind of zero sample classification based on low-rank representation and manifold regularization provided in this embodiment The part classifying result schematic diagram of method.Sample expression in figure with a line is assigned in same class, wherein wrong error symbol × Sample be classification error sample, other samples are the correct sample of classifying.
In the present embodiment, aPY data set may be selected in data set, and MATLAB R2017a, operation system may be selected in experiment porch It unites and 10 Education Edition of Windows may be selected, Intel (R) Core (TM) i7-6700K CPU@4.00GHz may be selected in processor, interior Deposit optional 32.0GB.
The zero sample classification method based on low-rank representation and manifold regularization of the present embodiment can effectively overcome tradition point The class method limitation low for nicety of grading when sample size is few, sample label information is lost, obtains invisible class More accurate semantic expressiveness on data set enhances the descriptive power to data characteristics, can effectively improve zero sample classification Precision solves currently when the label information for lacking enough training samples or sample is lost, and the low technology of nicety of grading is asked Topic.
The above, above embodiments are only to illustrate method of the invention, rather than its limitations；Although referring to aforementioned reality Applying example, invention is explained in detail, those skilled in the art should understand that：It still can be to aforementioned each Method documented by embodiment is modified or equivalent replacement of some of the technical features；And these modification or Replacement does not make the essence of correlation method be detached from the spirit and scope of the method for the present invention.

Claims (3)

1. a kind of zero sample classification method based on low-rank representation and manifold regularization, which is characterized in that include the following steps：
Step 1：Calculate the visual signature X of sample in visible class data setsWith semantic expressiveness AsBetween mapping relationship f, i.e. f:Xs →As, wherein visible class data set is For the visual signature of sample in visible class data set, p is the dimension of sample visual signature,For visible class The semantic expressiveness of sample in data set, q are the dimension that each sample corresponds to semantic expressiveness, csFor the class of visible class data set sample Not total, m is the total sample number of visible class data set；
Step 2：The semantic expressiveness A of sample in invisible class data set is calculated using mapping relationship fu, wherein invisible class data set ForFor sample in invisible class data set Visual signature andcuFor the classification sum of invisible class data set sample, n is the sample of invisible class data set Sum,For the invisible class data set X being calculateduSemantic expressiveness,
Step 3：Calculate the non-negative sparse low-rank representation Z of the Laplace regularization of sample in invisible class data set；
Step 4：Weight matrix W and Laplacian Matrix L is calculated using low-rank representation Z；
Step 5：Manifold regularization is introduced, the noise of the semantic expressiveness in invisible class data set is removed；
Step 6：Using the semantic expressiveness in the invisible class data set after denoising, the mark of sample in invisible class data set is predicted Label realize sample classification.
2. a kind of zero sample classification method based on low-rank representation and manifold regularization according to claim 1, feature It is, the expression of the non-negative sparse low-rank representation Z of the Laplace regularization of sample in invisible class data set is calculated in step 3 Formula is：
s.t. Xu=XuZ+E
Z≥0
||Z||0≤T
Wherein E is noise, and α is the first pre-setting tuning parameters, and β is the second pre-setting tuning parameters, | | | |*Indicate nuclear norm, | | ||1Indicate l1Norm, tr () indicate trace function, and Z >=0 ensure that the non-negative characteristic of matrix Z, | | Z | |0≤ T ensure that matrix Z Sparse characteristic.
3. zero sample classification method according to claim 1, which is characterized in that introduce manifold regularization in step 5, remove The formula of the noise of semantic expressiveness in invisible class data set is：
Wherein, I is unit matrix, and λ is third pre-setting tuning parameters,For the semantic table in the invisible class data set after denoising Show.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110427967A (en) * 2019-06-27 2019-11-08 中国矿业大学 The zero sample image classification method based on embedded feature selecting semanteme self-encoding encoder

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104866871A (en) * 2015-06-02 2015-08-26 西安电子科技大学 Projection structure sparse coding-based hyperspectral image classification method
CN105740912A (en) * 2016-02-03 2016-07-06 苏州大学 Nuclear norm regularization based low-rank image characteristic extraction identification method and system
US20160307072A1 (en) * 2015-04-17 2016-10-20 Nec Laboratories America, Inc. Fine-grained Image Classification by Exploring Bipartite-Graph Labels
CN106485271A (en) * 2016-09-30 2017-03-08 天津大学 A kind of zero sample classification method based on multi-modal dictionary learning
CN107491788A (en) * 2017-08-21 2017-12-19 天津大学 A kind of zero sample classification method based on dictionary learning

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160307072A1 (en) * 2015-04-17 2016-10-20 Nec Laboratories America, Inc. Fine-grained Image Classification by Exploring Bipartite-Graph Labels
CN104866871A (en) * 2015-06-02 2015-08-26 西安电子科技大学 Projection structure sparse coding-based hyperspectral image classification method
CN105740912A (en) * 2016-02-03 2016-07-06 苏州大学 Nuclear norm regularization based low-rank image characteristic extraction identification method and system
CN106485271A (en) * 2016-09-30 2017-03-08 天津大学 A kind of zero sample classification method based on multi-modal dictionary learning
CN107491788A (en) * 2017-08-21 2017-12-19 天津大学 A kind of zero sample classification method based on dictionary learning

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
C. H. LAMPERT等: "Attribute-based classification for zero-shot visual object categorization", 《TPAMI》 *
魏杰等: "基于视觉特征低维嵌入的细粒度图像分类", 《计算机辅助设计与图形学学报》 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110427967A (en) * 2019-06-27 2019-11-08 中国矿业大学 The zero sample image classification method based on embedded feature selecting semanteme self-encoding encoder

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