Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/657856
Title: Discriminative Transfer Subspace Learning via Low-Rank and Sparse Representation
Authors: Yong Xu;Xiaozhao Fang;Jian Wu;Xuelong Li;David Zhang
subject: target domain|subspace learning|low-rank and sparse constraints|Source domain|knowledge transfer,
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/168849
http://localhost/handle/Hannan/657856
ISSN: 1057-7149
1941-0042
volume: 25
issue: 2
Appears in Collections:2016

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Title: Discriminative Transfer Subspace Learning via Low-Rank and Sparse Representation
Authors: Yong Xu;Xiaozhao Fang;Jian Wu;Xuelong Li;David Zhang
subject: target domain|subspace learning|low-rank and sparse constraints|Source domain|knowledge transfer,
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/168849
http://localhost/handle/Hannan/657856
ISSN: 1057-7149
1941-0042
volume: 25
issue: 2
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7360924.pdf4.2 MBAdobe PDFThumbnail
Preview File
Title: Discriminative Transfer Subspace Learning via Low-Rank and Sparse Representation
Authors: Yong Xu;Xiaozhao Fang;Jian Wu;Xuelong Li;David Zhang
subject: target domain|subspace learning|low-rank and sparse constraints|Source domain|knowledge transfer,
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/168849
http://localhost/handle/Hannan/657856
ISSN: 1057-7149
1941-0042
volume: 25
issue: 2
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7360924.pdf4.2 MBAdobe PDFThumbnail
Preview File