Robust Semi-Supervised Subspace Clustering via Non-Negative Low-Rank Representation
文献类型：期刊论文
| 作者 | Fang, Xiaozhao1; Xu, Yong1,2; Li, Xuelong3 ; Lai, Zhihui1; Wong, Wai Keung4,5
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| 刊名 | ieee transactions on cybernetics
 |
| 出版日期 | 2016-08-01 |
| 卷号 | 46期号:8页码:1828-1838 |
| 关键词 | Affinity matrix low-rank representation (LRR) subspace clustering supervision information |
| ISSN号 | 2168-2267 |
| 产权排序 | 3 |
| 英文摘要 | low-rank representation (lrr) has been successfully applied in exploring the subspace structures of data. however, in previous lrr-based semi-supervised subspace clustering methods, the label information is not used to guide the affinity matrix construction so that the affinity matrix cannot deliver strong discriminant information. moreover, these methods cannot guarantee an overall optimum since the affinity matrix construction and subspace clustering are often independent steps. in this paper, we propose a robust semi-supervised subspace clustering method based on non-negative lrr (nnlrr) to address these problems. by combining the lrr framework and the gaussian fields and harmonic functions method in a single optimization problem, the supervision information is explicitly incorporated to guide the affinity matrix construction and the affinity matrix construction and subspace clustering are accomplished in one step to guarantee the overall optimum. the affinity matrix is obtained by seeking a non-negative low-rank matrix that represents each sample as a linear combination of others. we also explicitly impose the sparse constraint on the affinity matrix such that the affinity matrix obtained by nnlrr is non-negative low-rank and sparse. we introduce an efficient linearized alternating direction method with adaptive penalty to solve the corresponding optimization problem. extensive experimental results demonstrate that nnlrr is effective in semi-supervised subspace clustering and robust to different types of noise than other state-of-the-art methods. |
| WOS标题词 | science & technology ; technology |
| 类目[WOS] | computer science, artificial intelligence ; computer science, cybernetics |
| 研究领域[WOS] | computer science |
| 关键词[WOS] | multilabel image classification ; feature-selection ; algorithm ; recognition ; framework ; graph |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000379984500011 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/28170]  |
| 专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
| 作者单位 | 1.Harbin Inst Technol, Shenzhen Grad Sch, Biocomp Res Ctr, Shenzhen 518055, Guangdong, Peoples R China 2.Key Lab Network Oriented Intelligent Computat, Shenzhen 518055, Guangdong, Peoples R China 3.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr Opt IMagery Anal & Learning, Xian 710119, Shaanxi, Peoples R China 4.Hong Kong Polytech Univ, Inst Text & Clothing, Hong Kong, Hong Kong, Peoples R China 5.Hong Kong Polytech Univ, Shenzhen Res Inst, Shenzhen 518055, Peoples R China |
| 推荐引用方式 GB/T 7714 | Fang, Xiaozhao,Xu, Yong,Li, Xuelong,et al. Robust Semi-Supervised Subspace Clustering via Non-Negative Low-Rank Representation[J]. ieee transactions on cybernetics,2016,46(8):1828-1838. |
| APA | Fang, Xiaozhao,Xu, Yong,Li, Xuelong,Lai, Zhihui,&Wong, Wai Keung.(2016).Robust Semi-Supervised Subspace Clustering via Non-Negative Low-Rank Representation.ieee transactions on cybernetics,46(8),1828-1838. |
| MLA | Fang, Xiaozhao,et al."Robust Semi-Supervised Subspace Clustering via Non-Negative Low-Rank Representation".ieee transactions on cybernetics 46.8(2016):1828-1838. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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