Kernel truncated regression representation for robust subspace clustering

Zhen，Liangli1; Peng，Dezhong2,3; Wang，Wei2,4; Yao，Xin5,6

2020-07-01

10.1016/j.ins.2020.03.033

INFORMATION SCIENCES   影响因子和分区

0020-0255

1872-6291

Subspace clustering aims to group data points into multiple clusters of which each corresponds to one subspace. Most existing subspace clustering approaches assume that input data lie on linear subspaces. In practice, however, this assumption usually does not hold. To achieve nonlinear subspace clustering, we propose a novel method, called kernel truncated regression representation. Our method consists of the following four steps: 1) projecting the input data into a hidden space, where each data point can be linearly represented by other data points; 2) calculating the linear representation coefficients of the data representations in the hidden space; 3) truncating the trivial coefficients to achieve robustness and block-diagonality; and 4) executing the graph cutting operation on the coefficient matrix by solving a graph Laplacian problem. Our method has the advantages of a closed-form solution and the capacity of clustering data points that lie on nonlinear subspaces. The first advantage makes our method efficient in handling large-scale datasets, and the second one enables the proposed method to conquer the nonlinear subspace clustering challenge. Extensive experiments on six benchmarks demonstrate the effectiveness and the efficiency of the proposed method in comparison with current state-of-the-art approaches.

Kernel techniques Kernel truncated regression Nonlinear subspace clustering Spectral clustering

SCI ; EI

英语

其他

National Natural Science Foundation of China[61432012][61329302][61625204][61971296][U19A2078] ; Engineering and Physical Sciences Research Council (EPSRC) of U.K.[EP/J017515/1] ; Ministry of Education & China Mobile Research Funding[MCM20180405] ; Sichuan Science and Technology Planning Projects[2019YFG0495][2019YFH0075] ; Program for Guangdong Introducing Innovative and Entrepreneurial Teams[2017ZT07X386] ; Shenzhen Peacock Plan[KQTD2016112514355531] ; Science and Technology Innovation Committee Foundation of Shenzhen[ZDSYS201703031748284] ; Program for University Key Laboratory of Guangdong Province[2017KSYS008]

Computer Science

Computer Science, Information Systems

WOS:000530095300005

ELSEVIER SCIENCE INC

20201308336576

Large dataset ; Graphic methods ; Input output programs ; Regression analysis

Computer Programming:723.1 ; Data Processing and Image Processing:723.2 ; Information Sources and Analysis:903.1 ; Mathematical Statistics:922.2

COMPUTER SCIENCE

2-s2.0-85082000991

Scopus

1.Institute of High Performance Computing,A*STAR,138632,Singapore
2.College of Computer Science,Sichuan University,Chengdu,610065,China
3.Peng Cheng Laboratory,Shenzhen,518055,China
4.Chengdu Sobey Digital Technology Co.,Ltd.,Chengdu,610041,China
5.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
6.CERCIA,School of Computer Science,University of Birmingham,Birmingham,B15 2TT,United Kingdom

Zhen，Liangli,Peng，Dezhong,Wang，Wei,et al. Kernel truncated regression representation for robust subspace clustering[J]. INFORMATION SCIENCES,2020,524:59-76.

Zhen，Liangli,Peng，Dezhong,Wang，Wei,&Yao，Xin.(2020).Kernel truncated regression representation for robust subspace clustering.INFORMATION SCIENCES,524,59-76.

Zhen，Liangli,et al."Kernel truncated regression representation for robust subspace clustering".INFORMATION SCIENCES 524(2020):59-76.