Computer Science ›› 2022, Vol. 49 ›› Issue (8): 86-96.doi: 10.11896/jsjkx.210700124

• Database & Big Data & Data Science • Previous Articles     Next Articles

Unsupervised Multi-view Feature Selection Based on Similarity Matrix Learning and Matrix Alignment

LI Bin, WAN Yuan   

  1. School of Science,Wuhan University of Technology,Wuhan 430070,China
  • Received:2021-07-13 Revised:2022-02-27 Published:2022-08-02
  • About author:LI Bin,born in 1997,postgraduate.His main research interests include machine learning,pattern recognition and dimension reduction of high dimensional image features.
    WAN Yuan,born in 1976,Ph.D,professor,is a member of China Computer Federation.Her main research interests include machine learning,image processing and pattern recognition.
  • Supported by:
    Fundamental Research Funds for the Central Universities(2021III030JC).

Abstract: Multi-view feature selection improves the efficiency of classification,clustering and other learning tasks by fusing information from multiple views to obtain representative feature subsets.However,the features of different views that describe objects are complex and interrelated.Simply searching subset of features from original space partly solves the problem of dimension,but it barely obtains the latent structural information and association information among features.Besides,using fixed similarity matrix and projection matrix is prone to lose the correlation between different views.To solve these problems,an unsupervised multi-view feature selection algorithm based on similarity matrix learning and matrix alignment(SMLMA)is proposed.Firstly,the similarity matrix based on all views is constructed,and the consistent similarity matrix and projection matrix are obtained by mani-fold learning,to explore and reserve the structural information of data to the greatest extent.Then,the matrix alignment method is used to maximize the correlation between the similarity matrix and the kernel matrix,for the purpose of using the correlation between different views and reducing the information redundancy of feature subset.Finally,the Armijo searching method is introduced to obtain the convergence result quickly.Experimental results on four datasets(Caltech-7,NUS-WIDE-OBJ,Toy Animal and MSRC-v1)show that,compared with single view feature selection and some multi-view feature selection methods,the accuracy of SMLMA is averagely improved by about 7.54%.The proposed algorithm well retains the structural information of data and the correlation between multi-view features,and captures more high-quality features.

Key words: Feature selection, Matrix alignment, Multi-view, Similarity matrix, Unsupervised

CLC Number: 

  • TP181
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