Discriminative Transfer Subspace Learning via Low-Rank and Sparse Representation

CORC > 西安光学精密机械研究所 > 中国科学院西安光学精密机械研究所 > 光学影像学习与分析中心

	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
刊名	ieee transactions on image processing
	2016-02-01
卷号	25 期号:2 页码:850-863
关键词	Source domain target domain low-rank and sparse constraints knowledge transfer subspace learning
ISSN号	1057-7149
产权排序	3
英文摘要	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.
学科主题	computer science, artificial intelligence ; engineering, electrical & electronic
WOS标题词	science & technology ; technology
类目[WOS]	computer science, artificial intelligence ; engineering, electrical & electronic
研究领域[WOS]	computer science ; engineering
关键词[WOS]	unsupervised domain adaptation ; face recognition ; dimensionality reduction ; fuzzy system ; regularization ; classification
收录类别	SCI
语种	英语
WOS记录号	WOS:000383905800028
内容类型	期刊论文
源URL	[http://ir.opt.ac.cn/handle/181661/28367]
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, 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.