A Survey of Sparse Representation: Algorithms and Applications

doi:10.1109/ACCESS.2015.2430359

OPT OpenIR > 光谱成像技术研究室

	A Survey of Sparse Representation: Algorithms and Applications
	Zhang, Zheng 1,2; Xu, Yong 1,2; Yang, Jian 3; Li, Xuelong4 ; Zhang, David 5
作者部门	光学影像学习与分析中心
	2015
发表期刊	IEEE ACCESS
ISSN	2169-3536
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
DOI	10.1109/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	被引频次：813[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.opt.ac.cn/handle/181661/27315
专题	光谱成像技术研究室
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
推荐引用方式 GB/T 7714	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.

条目包含的文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
A Survey of Sparse R（4920KB）	期刊论文	作者接受稿	限制开放	CC BY-NC-SA	请求全文