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
[1]JAMIESON K,BALAKRISHNAN H,TAY Y C.Sift:A MAC Protocol for Event-Driven Wireless Sensor Networks [C]//European Workshop on Wireless Sensor Networks.Berlin:Sprin-ger,2006:260-275.
[2]WANG X,HAN T X,YAN S.An HOG-LBP human detectorwith partial occlusion handling [C]//2009 IEEE 12th International Conference on Computer Vision.IEEE,2009:32-39.
[3]TAN X,TRIGGS B.Fusing Gabor and LBP Feature Sets for Kernel-Based Face Recognition [C]//International Workshop on Analysis and Modeling of Faces and Gestures.Berlin:Sprin-ger,2007:235-249.
[4]LI L,CAI M.Drug target prediction by multi-view low rank embedding[J].IEEE/ACM Transactions on Computational Biology and Bioinformatics,2017,16(5):1712-1721.
[5]ZHANG H,WU D,NIE F,et al.Multilevel projections withadaptive neighbor graph for unsupervised multi-view feature selection [J].Information Fusion,2020,70(3):129-140.
[6]SUN S.A survey of multi-view machine learning [J].NeuralComputing and Applications,2013,23(7):2031-2038.
[7]ZHAO J,XIE X,XU X,et al.Multi-view learning overview:Recent progress and new challenges [J].Information Fusion,2017,38:43-54.
[8]XIE X.Regularized multi-view least squares twin support vector machines [J].Applied Intelligence,2018,48(9):3108-3115.
[9]LI X,ZHANG H,WANG R,et al.Multi-view Clustering:AScalable and Parameter-free Bipartite Graph Fusion Method [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2020,44(1):330-344.
[10]XIE X,SUN S.General multi-view learning with maximum entropy discrimination [J].Neurocomputing,2019,332:184-192.
[11]YIN J,SUN S.Multiview Uncorrelated Locality Preserving Projection[J].IEEE Transactions on Neural Networks and Lear-ning Systems,2020,31(9):3442-3455.
[12]LIN G F,ZHU H,FAN C X,et al.Multi-cluster Feature Selection Based on Grassmann Manifold [J].Computer Engineering,2012,16:3511-3518.
[13]WAN Y,CHEN X,ZHANG J.Global and intrinsic geometricstructure embedding for unsupervised feature selection [J].Expert Systems with Applications,2018,93(March):134-142.
[14]HE X,CAI D,NIYOGI P.Laplacian Score for Feature Selection [C]//Advances in Neural Information Processing Systems.2005:1-8.
[15]ZHAO Z,LIU H.Spectral feature selection for supervised and unsupervised learning [C]//Proceedings of the 24th International Conference on Machine learning.Association for Computing Machinery,2007:1151-1157.
[16]ZHAO Z,WANG L,LIU H.Efficient spectral feature selection with minimum redundancy [C]//Proceedings of the AAAI Conference on Artificial Intelligence.2010:1-6.
[17]HE X.Locality preserving projections [J].Advances in NeuralInformation Processing Systems,2003,16(1):186-197.
[18]BELKIN M,NIYOGI P.Laplacian Eigenmaps for Dimensionality Reduction and Data Representation [J].Neural Computation,2003,15(6):1373-1396.
[19]LIU X,WANG L,ZHANG J,et al.Global and local structurepreservation for feature selection [J].IEEE Transactions on Neural Networks and Learning Systems,2013,25(6):1083-1095.
[20]DU L,SHEN Y D.Unsupervised feature selection with adaptive structure learning [C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2015:209-218.
[21]FENG Y,XIAO J,ZHUANG Y,et al.Adaptive UnsupervisedMulti-view Feature Selection for Visual Concept Recognition [C]//Asian Conference on Computer Vision.Berlin:Springer,2012:343-357.
[22]LI J,HU X,TANG J,et al.Unsupervised streaming feature selection in social media [C]//Proceedings of the 24th ACM International on Conference on Information and Knowledge Mana-gement.2015:1041-1050.
[23]WANG Z,FENG Y F,TIAN Q,et al.Adaptive multi-view feature selection for human motion retrieval [J].Signal Proces-sing,2016,100(120):691-701.
[24]HOU C,NIE F,TAO H,et al.Multi-View Unsupervised Feature Selection with Adaptive Similarity and View Weight [J].IEEE Transactions on Knowledge and Data Engineering,2017,29(9):1998-2011.
[25]SHAO W,HE L,LU C T,et al.Online Unsupervised Multi-view Feature Selection [J].2016 IEEE 16th International Conference on Data Mining(ICDM).IEEE,2016:1203-1208.
[26]DING Z,FU Y.Low-rank common subspace for multi-viewlearning [C]//2014 IEEE International Conference on Data Mining.IEEE,2014:110-119.
[27]WAN Y,SUN S Z,ZENG C,et al.Adaptive Similarity Embedding For Unsupervised Multi-View Feature Selection [J].IEEE Transactions on Knowledge and Data Engineering,2020,33(10):3338-3350.
[28]XU C,TAO D,XU C,et al.Multi-View Intact Space Learning [J].IEEE Transactions on Pattern Analysis aNd Machine Intelligence,2015,37(12):2531-2544.
[29]TANG C,CHEN J,LIU X,et al.Consensus Learning GuidedMulti-view Unsupervised Feature Selection [J].Knowledge-Based Systems,2018,160:49-60.
[30]DONG X,ZHU L,SONG X,et al.Adaptive collaborative similarity learning for unsupervised multi-view feature selection [C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence.2018:2064-2070.
[31]SHI C,GU Z,DUAN C,et al.Multi-view Adaptive Semi-supervised Feature Selection with the self-paced learning [J].Signal Processing,2020,168:107332.
[32]ZHENG X,CHEN J,TANG C,et al.Single-Cell RNA-Sequencing Data Clustering via Locality Preserving Kernel Matrix Alignment[J].IEEE Access,2020,8:201577-201594.
[33]HUANG F,WU Z Z.Analysis and comparison of several conjugate gradient methods based on Armijo search step[J].Journal of Chengdu Institute of Information Engineering,2019,34(2):209-215.
[34]ZHANG R,NIE F,LI X,et al.Feature selection with multi-view data:A survey [J].Information Fusion,2019,50:158-167.
[35]TANG C,ZHU X,LIU X,et al.Cross-View Local Structure Preserved Diversity and Consensus Learning for Multi-View Unsupervised Feature Selection [C]//The AAAI Conference on Artificial Intelligence.2019:5101-5108.
[36]XB A,LEI Z A,CHENG L A,et al.Multi-view feature selection via Nonnegative Structured Graph Learning [J].Neurocompu-ting,2020,387:110-122.
[37]STREHL A,GHOSH J,CARDIE C,et al.Cluster Ensembles:A Knowledge Reuse Framework for Combining Multiple Partitions [J].Journal of Machine Learning Research,2002,3(3):583-617.
[38]YE X Y,YE X Y,ZHOU H.Feature selection based on in-fluence community detection and ant colony algorithm[J].Computer Engineering and Design,2019,40(9):2684-2691.
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