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Discriminative Transfer Subspace Learning via Low-Rank and Sparse Representation
文献类型：期刊论文
| 作者 | Xu, Yong1,2; Fang, Xiaozhao1; Wu, Jian1; Li, Xuelong3 ; Zhang, David4
|
| 刊名 | ieee transactions on image processing
 |
| 出版日期 | 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. |
| WOS标题词 | science & technology ; technology |
| 学科主题 | computer science, artificial intelligence ; engineering, electrical & electronic |
| 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 |
| 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. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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