| Information Theoretic Subspace Clustering | |
He, Ran1 ; Wang, Liang1; Sun, Zhenan1; Zhang, Yingya2; Li, Bo3; Bo LI
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| 发表期刊 | IEEE Transactions on Neural Networks and Learning Systems
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| 2016-12-01 | |
| 卷号 | 27期号:12页码:2643-2655 |
| 文章类型 | Article |
| 摘要 | This 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. |
| 关键词 | Correntropy Group Sparsity Low-rank Representation (Lrr) Subspace Clustering |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1109/TNNLS.2015.2500600 |
| 关键词[WOS] | FACE RECOGNITION ; SEGMENTATION ; CORRENTROPY ; REPRESENTATION ; MINIMIZATION ; RECOVERY ; SIGNAL |
| 收录类别 | SCI |
| 语种 | 英语 |
| 项目资助者 | Strategic 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研究方向 | Computer Science ; Engineering |
| WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
| WOS记录号 | WOS:000388919600015 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.ia.ac.cn/handle/173211/12008 |
| 专题 | 模式识别实验室 |
| 通讯作者 | Bo LI |
| 作者单位 | 1.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 |
| 第一作者单位 | 模式识别国家重点实验室 |
| 推荐引用方式 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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| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Information Theoreti（2269KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 | |
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