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Half-quadratic based Iterative Minimization for Robust Sparse Representation
Ran He(赫然)1,2; Weishi Zheng3; Tieniu Tan1,2; Zhenan Sun1,2; He, Ran
发表期刊IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
2014-02-01
卷号36期号:2页码:261-275
文章类型Article
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.
关键词l(1)-minimization Half-quadratic Optimization Sparse Representation M-estimator Correntropy
WOS标题词Science & Technology ; Technology
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研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号WOS:000328899500005
引用统计
被引频次:234[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/3800
专题智能感知与计算
通讯作者He, Ran
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
第一作者单位模式识别国家重点实验室
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GB/T 7714
Ran He,Weishi Zheng,Tieniu Tan,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 Ran He,Weishi Zheng,Tieniu Tan,Zhenan Sun,&He, Ran.(2014).Half-quadratic based Iterative Minimization for Robust Sparse Representation.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,36(2),261-275.
MLA Ran He,et al."Half-quadratic based Iterative Minimization for Robust Sparse Representation".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 36.2(2014):261-275.
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