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Information Theoretic Subspace Clustering
He, Ran1; Wang, Liang1; Sun, Zhenan1; Zhang, Yingya2; Li, Bo3; Bo LI
Source PublicationIEEE Transactions on Neural Networks and Learning Systems
AbstractThis paper addresses the problem of grouping the data points sampled from a union of multiple subspaces in the presence of outliers. Information theoretic objective functions are proposed to combine structured low-rank representations (LRRs) to capture the global structure of data and information theoretic measures to handle outliers. In theoretical part, we point out that group sparsity-induced measures (l(2,1)-norm, l(alpha)-norm, and correntropy) can be justified from the viewpoint of half-quadratic (HQ) optimization, which facilitates both convergence study and algorithmic development. In particular, a general formulation is accordingly proposed to unify HQ-based group sparsity methods into a common framework. In algorithmic part, we develop information theoretic subspace clustering methods via correntropy. With the help of Parzen window estimation, correntropy is used to handle either outliers under any distributions or sample-specific errors in data. Pairwise link constraints are further treated as a prior structure of LRRs. Based on the HQ framework, iterative algorithms are developed to solve the nonconvex information theoretic loss functions. Experimental results on three benchmark databases show that our methods can further improve the robustness of LRR subspace clustering and outperform other state-of-the-art subspace clustering methods.
KeywordCorrentropy Group Sparsity Low-rank Representation (Lrr) Subspace Clustering
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Funding OrganizationStrategic Priority Research Program of the Chinese Academy of Sciences (CAS)(XDB02000000) ; State Key Laboratory of Software Development Environment(SKLSDE-2015ZX-14) ; National Basic Research Program of China(2012CB316300) ; National Natural Science Foundation of China(61473289 ; Youth Innovation Promotion Association through CAS(2015190) ; 61175003)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000388919600015
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Cited Times:11[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorBo LI
Affiliation1.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Ctr Res Intelligent Percept & Comp, Natl Lab Pattern Recognit,Inst Automat, Beijing 100190, Peoples R China
2.Alibaba Grp, Beijing 100190, Peoples R China
3.Beihang Univ, Sch Comp Sci & Engn, Beijing 100191, Peoples R China
Recommended Citation
GB/T 7714
He, Ran,Wang, Liang,Sun, Zhenan,et al. Information Theoretic Subspace Clustering[J]. IEEE Transactions on Neural Networks and Learning Systems,2016,27(12):2643-2655.
APA He, Ran,Wang, Liang,Sun, Zhenan,Zhang, Yingya,Li, Bo,&Bo LI.(2016).Information Theoretic Subspace Clustering.IEEE Transactions on Neural Networks and Learning Systems,27(12),2643-2655.
MLA He, Ran,et al."Information Theoretic Subspace Clustering".IEEE Transactions on Neural Networks and Learning Systems 27.12(2016):2643-2655.
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