Discriminative Transfer Subspace Learning via Low-Rank and Sparse Representation

doi:10.1109/TIP.2015.2510498

OPT OpenIR > 光谱成像技术研究室

	Discriminative Transfer Subspace Learning via Low-Rank and Sparse Representation
	Xu, Yong 1,2; Fang, Xiaozhao 1; Wu, Jian 1; Li, Xuelong3 ; Zhang, David 4
作者部门	光学影像学习与分析中心
	2016-02-01
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN	1057-7149
卷号	25 期号:2 页码:850-863
产权排序	3
摘要	In this paper, we address the problem of unsupervised domain transfer learning in which no labels are available in the target domain. We use a transformation matrix to transfer both the source and target data to a common subspace, where each target sample can be represented by a combination of source samples such that the samples from different domains can be well interlaced. In this way, the discrepancy of the source and target domains is reduced. By imposing joint low-rank and sparse constraints on the reconstruction coefficient matrix, the global and local structures of data can be preserved. To enlarge the margins between different classes as much as possible and provide more freedom to diminish the discrepancy, a flexible linear classifier (projection) is obtained by learning a non-negative label relaxation matrix that allows the strict binary label matrix to relax into a slack variable matrix. Our method can avoid a potentially negative transfer by using a sparse matrix to model the noise and, thus, is more robust to different types of noise. We formulate our problem as a constrained low-rankness and sparsity minimization problem and solve it by the inexact augmented Lagrange multiplier method. Extensive experiments on various visual domain adaptation tasks show the superiority of the proposed method over the state-of-the art methods. The MATLAB code of our method will be publicly available at http://www.yongxu.org/lunwen.html.
文章类型	Article
关键词	Source Domain Target Domain Low-rank And Sparse Constraints Knowledge Transfer Subspace Learning
学科领域	Computer Science, Artificial Intelligence
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TIP.2015.2510498
收录类别	SCI
关键词[WOS]	UNSUPERVISED DOMAIN ADAPTATION ; FACE RECOGNITION ; DIMENSIONALITY REDUCTION ; FUZZY SYSTEM ; REGULARIZATION ; CLASSIFICATION
语种	英语
WOS研究方向	Computer Science ; Engineering
项目资助者	National Basic Research Program of China (973 Program)(2012CB316400) ; National Natural Science Foundation of China(61370163 ; Shaanxi Key Innovation Team of Science and Technology(2012KCT-04) ; 61332011)
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000383905800028
引用统计	被引频次：235[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/181661/28367
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, Peoples R China 2.Key Lab Network Oriented Intelligent Computat, Shenzhen 518055, Peoples R China 3.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr Opt Imagery Anal & Learning, State Key Lab Transient Opt & Photon, Xian 710119, Peoples R China 4.Hong Kong Polytech Univ, Biometr Res Ctr, Hong Kong, Hong Kong, Peoples R China
推荐引用方式 GB/T 7714	Xu, Yong,Fang, Xiaozhao,Wu, Jian,et al. Discriminative Transfer Subspace Learning via Low-Rank and Sparse Representation[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2016,25(2):850-863.
APA	Xu, Yong,Fang, Xiaozhao,Wu, Jian,Li, Xuelong,&Zhang, David.(2016).Discriminative Transfer Subspace Learning via Low-Rank and Sparse Representation.IEEE TRANSACTIONS ON IMAGE PROCESSING,25(2),850-863.
MLA	Xu, Yong,et al."Discriminative Transfer Subspace Learning via Low-Rank and Sparse Representation".IEEE TRANSACTIONS ON IMAGE PROCESSING 25.2(2016):850-863.

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