Robust Semi-Supervised Subspace Clustering via Non-Negative Low-Rank Representation

doi:10.1109/TCYB.2015.2454521

OPT OpenIR > 光谱成像技术研究室

	Robust Semi-Supervised Subspace Clustering via Non-Negative Low-Rank Representation
	Fang, Xiaozhao 1; Xu, Yong 1,2; Li, Xuelong3 ; Lai, Zhihui 1; Wong, Wai Keung 4,5
作者部门	光学影像学习与分析中心
	2016-08-01
发表期刊	IEEE TRANSACTIONS ON CYBERNETICS
ISSN	2168-2267
卷号	46 期号:8 页码:1828-1838
产权排序	3
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.
文章类型	Article
关键词	Affinity Matrix Low-rank Representation (Lrr) Subspace Clustering Supervision Information
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TCYB.2015.2454521
收录类别	SCI ; EI
关键词[WOS]	MULTILABEL IMAGE CLASSIFICATION ; FEATURE-SELECTION ; ALGORITHM ; RECOGNITION ; FRAMEWORK ; GRAPH
语种	英语
WOS研究方向	Computer Science
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(2014M560264) ; Shaanxi Key Innovation Team of Science and Technology(2012KCT-04) ; 61233011 ; JCYJ20130329151843309 ; 61332011) ; JCYJ20140417172417174 ; CXZZ20140904154910774)
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号	WOS:000379984500011
引用统计	被引频次：102[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/181661/28170
专题	光谱成像技术研究室
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.

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
Robust Semi-Supervis（1675KB）	期刊论文	作者接受稿	限制开放	CC BY-NC-SA	请求全文