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A Survey of Sparse Representation: Algorithms and Applications
Zhang, Zheng1,2; Xu, Yong1,2; Yang, Jian3; Li, Xuelong4; Zhang, David5
作者部门光学影像学习与分析中心
2015
发表期刊IEEE ACCESS
ISSN2169-3536
卷号3页码:490-530
产权排序4
摘要Sparse representation has attracted much attention from researchers in fields of signal processing, image processing, computer vision, and pattern recognition. Sparse representation also has a good reputation in both theoretical research and practical applications. Many different algorithms have been proposed for sparse representation. The main purpose of this paper is to provide a comprehensive study and an updated review on sparse representation and to supply guidance for researchers. The taxonomy of sparse representation methods can be studied from various viewpoints. For example, in terms of different norm minimizations used in sparsity constraints, the methods can be roughly categorized into five groups: 1) sparse representation with L-0-norm minimization; 2) sparse representation with L-p-norm (0 < p < 1) minimization; 3) sparse representation with L-1-norm minimization; 4) sparse representation with 12,1-norm minimization; and 5) sparse representation with 12-norm minimization. In this paper, a comprehensive overview of sparse representation is provided. The available sparse representation algorithms can also be empirically categorized into four groups: 1) greedy strategy approximation; 2) constrained optimization; 3) proximity algorithm-based optimization; and 4) homotopy algorithm-based sparse representation. The rationales of different algorithms in each category are analyzed and a wide range of sparse representation applications are summarized, which could sufficiently reveal the potential nature of the sparse representation theory. In particular, an experimentally comparative study of these sparse representation algorithms was presented.
文章类型Article
关键词Sparse Representation Compressive Sensing Greedy Algorithm Constrained Optimization Proximal Algorithm Homotopy Algorithm Dictionary Learning
学科领域Computer Science, Information Systems
WOS标题词Science & Technology ; Technology
DOI10.1109/ACCESS.2015.2430359
收录类别SCI ; EI
关键词[WOS]INTERIOR-POINT METHOD ; ORTHOGONAL MATCHING PURSUIT ; ROBUST FACE RECOGNITION ; SINGLE-IMAGE SUPERRESOLUTION ; LINEAR INVERSE PROBLEMS ; CONSISTENT K-SVD ; VISUAL TRACKING ; SIGNAL RECOVERY ; LOW-RANK ; THRESHOLDING ALGORITHM
语种英语
WOS研究方向Computer Science ; Engineering ; Telecommunications
项目资助者National Natural Science Foundation of China(61370163 ; Shenzhen Municipal Science and Technology Innovation Council(JCYJ20130329151843309 ; China Post-Doctoral Science Foundation Funded Project(2014M560264) ; Shaanxi Key Innovation Team of Science and Technology(2012KCT-04) ; 61233011 ; JCYJ20140417172417174 ; 61332011) ; CXZZ20140904154910774)
WOS类目Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号WOS:000371388200037
引用统计
文献类型期刊论文
条目标识符http://ir.opt.ac.cn/handle/181661/27315
专题光学影像学习与分析中心
通讯作者Xu, Yong
作者单位1.Harbin Inst Technol, Shenzhen Grad Sch, Biocomp Res Ctr, Shenzhen 518055, Peoples R China
2.Key Lab Network Oriented Intelligent Computat, Shenzhen 518055, Peoples R China
3.Nanjing Univ Sci & Technol, Coll Comp Sci & Technol, Nanjing 210094, Jiangsu, Peoples R China
4.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr Opt Imagery Anal & Learning, State Key Lab Transient Opt & Photon, Xian 710119, Shaanxi, Peoples R China
5.Hong Kong Polytech Univ, Biometr Res Ctr, Hong Kong, Hong Kong, Peoples R China
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Zhang, Zheng,Xu, Yong,Yang, Jian,et al. A Survey of Sparse Representation: Algorithms and Applications[J]. IEEE ACCESS,2015,3:490-530.
APA Zhang, Zheng,Xu, Yong,Yang, Jian,Li, Xuelong,&Zhang, David.(2015).A Survey of Sparse Representation: Algorithms and Applications.IEEE ACCESS,3,490-530.
MLA Zhang, Zheng,et al."A Survey of Sparse Representation: Algorithms and Applications".IEEE ACCESS 3(2015):490-530.
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