| A Survey of Sparse Representation: Algorithms and Applications | |
Zhang, Zheng1,2; Xu, Yong1,2; Yang, Jian3; Li, Xuelong4 ; Zhang, David5
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| 作者部门 | 光学影像学习与分析中心 |
| 2015 | |
| 发表期刊 | IEEE ACCESS
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| 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 | |
| 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 |
| 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. |
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| A Survey of Sparse R（4920KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 请求全文 | |
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