Half-Quadratic-Based Iterative Minimization for Robust Sparse Representation

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	Half-Quadratic-Based Iterative Minimization for Robust Sparse Representation
	He, Ran 1,2; Zheng, Wei-Shi 3; Tan, Tieniu 1,2; Sun, Zhenan 1,2; Zhenan Sun; Tieniu Tan; Ran He(赫然)
刊名	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
	2014-02-01
卷号	36 期号:2 页码:261-275
关键词	l(1)-minimization half-quadratic optimization sparse representation M-estimator correntropy
英文摘要	Robust sparse representation has shown significant potential in solving challenging problems in computer vision such as biometrics and visual surveillance. Although several robust sparse models have been proposed and promising results have been obtained, they are either for error correction or for error detection, and learning a general framework that systematically unifies these two aspects and explores their relation is still an open problem. In this paper, we develop a half-quadratic ( HQ) framework to solve the robust sparse representation problem. By defining different kinds of half-quadratic functions, the proposed HQ framework is applicable to performing both error correction and error detection. More specifically, by using the additive form of HQ, we propose an l(1)-regularized error correction method by iteratively recovering corrupted data from errors incurred by noises and outliers; by using the multiplicative form of HQ, we propose an l(1)-regularized error detection method by learning from uncorrupted data iteratively. We also show that the l(1)-regularization solved by soft-thresholding function has a dual relationship to Huber M-estimator, which theoretically guarantees the performance of robust sparse representation in terms of M-estimation. Experiments on robust face recognition under severe occlusion and corruption validate our framework and findings.
WOS标题词	Science & Technology ; Technology
类目[WOS]	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
研究领域[WOS]	Computer Science ; Engineering
关键词[WOS]	LINEAR INVERSE PROBLEMS ; FACE RECOGNITION ; SIGNAL RECOVERY ; THRESHOLDING ALGORITHM ; CORRUPTED OBSERVATIONS ; PATTERN-RECOGNITION ; IMAGE-RESTORATION ; L(1)-MINIMIZATION ; RECONSTRUCTION ; CORRENTROPY
收录类别	SCI
语种	英语
WOS记录号	WOS:000328899500005
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/3800]
专题	自动化研究所_智能感知与计算研究中心
作者单位	1.Chinese Acad Sci, Ctr Res Intelligent Percept & Comp CRIPAC, Beijing 100190, Peoples R China 2.Chinese Acad Sci, NLPR, Inst Automat, Beijing 100190, Peoples R China 3.Sun Yat Sen Univ, Sch Informat Sci & Technol, Guangzhou 510260, Guangdong, Peoples R China
推荐引用方式 GB/T 7714	He, Ran,Zheng, Wei-Shi,Tan, Tieniu,et al. Half-Quadratic-Based Iterative Minimization for Robust Sparse Representation[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2014,36(2):261-275.
APA	He, Ran.,Zheng, Wei-Shi.,Tan, Tieniu.,Sun, Zhenan.,Zhenan Sun.,...&Ran He.(2014).Half-Quadratic-Based Iterative Minimization for Robust Sparse Representation.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,36(2),261-275.
MLA	He, Ran,et al."Half-Quadratic-Based Iterative Minimization for Robust Sparse Representation".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 36.2(2014):261-275.