Bregman Divergence-Based Regularization for Transfer Subspace Learning
The regularization principals [31] lead approximation schemes to deal with various learning problems, e.g., the regularization of the norm in a reproducing kernel Hilbert space for the ill-posed problem. In this paper, we present a family of subspace learning algorithms based on a new form of regula...
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| Published in: | IEEE transactions on knowledge and data engineering Vol. 22; no. 7; pp. 929 - 942 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York, NY
IEEE
01.07.2010
IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 1041-4347, 1558-2191 |
| Online Access: | Get full text |
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| Abstract | The regularization principals [31] lead approximation schemes to deal with various learning problems, e.g., the regularization of the norm in a reproducing kernel Hilbert space for the ill-posed problem. In this paper, we present a family of subspace learning algorithms based on a new form of regularization, which transfers the knowledge gained in training samples to testing samples. In particular, the new regularization minimizes the Bregman divergence between the distribution of training samples and that of testing samples in the selected subspace, so it boosts the performance when training and testing samples are not independent and identically distributed. To test the effectiveness of the proposed regularization, we introduce it to popular subspace learning algorithms, e.g., principal components analysis (PCA) for cross-domain face modeling; and Fisher's linear discriminant analysis (FLDA), locality preserving projections (LPP), marginal Fisher's analysis (MFA), and discriminative locality alignment (DLA) for cross-domain face recognition and text categorization. Finally, we present experimental evidence on both face image data sets and text data sets, suggesting that the proposed Bregman divergence-based regularization is effective to deal with cross-domain learning problems. |
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| AbstractList | The regularization principals [CHECK END OF SENTENCE] lead approximation schemes to deal with various learning problems, e.g., the regularization of the norm in a reproducing kernel Hilbert space for the ill-posed problem. In this paper, we present a family of subspace learning algorithms based on a new form of regularization, which transfers the knowledge gained in training samples to testing samples. In particular, the new regularization minimizes the Bregman divergence between the distribution of training samples and that of testing samples in the selected subspace, so it boosts the performance when training and testing samples are not independent and identically distributed. To test the effectiveness of the proposed regularization, we introduce it to popular subspace learning algorithms, e.g., principal components analysis (PCA) for cross-domain face modeling; and Fisher's linear discriminant analysis (FLDA), locality preserving projections (LPP), marginal Fisher's analysis (MFA), and discriminative locality alignment (DLA) for cross-domain face recognition and text categorization. Finally, we present experimental evidence on both face image data sets and text data sets, suggesting that the proposed Bregman divergence-based regularization is effective to deal with cross-domain learning problems. The regularization principals [31] lead approximation schemes to deal with various learning problems, e.g., the regularization of the norm in a reproducing kernel Hilbert space for the ill-posed problem. In this paper, we present a family of subspace learning algorithms based on a new form of regularization, which transfers the knowledge gained in training samples to testing samples. In particular, the new regularization minimizes the Bregman divergence between the distribution of training samples and that of testing samples in the selected subspace, so it boosts the performance when training and testing samples are not independent and identically distributed. To test the effectiveness of the proposed regularization, we introduce it to popular subspace learning algorithms, e.g., principal components analysis (PCA) for cross-domain face modeling; and Fisher's linear discriminant analysis (FLDA), locality preserving projections (LPP), marginal Fisher's analysis (MFA), and discriminative locality alignment (DLA) for cross-domain face recognition and text categorization. Finally, we present experimental evidence on both face image data sets and text data sets, suggesting that the proposed Bregman divergence-based regularization is effective to deal with cross-domain learning problems. |
| Author | Dacheng Tao Si Si Bo Geng |
| Author_xml | – sequence: 1 givenname: Si surname: Si fullname: Si, Si – sequence: 2 givenname: Dacheng surname: Tao fullname: Tao, Dacheng – sequence: 3 givenname: Bo surname: Geng fullname: Geng, Bo |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=22886081$$DView record in Pascal Francis |
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| Keywords | Image processing regularization Modeling Learning Alignment Vector space Classification Facies Hilbert space Ill posed problem Learning algorithm Dimensionality reduction Discriminant analysis Face recognition Locality Image databank Text Pattern recognition Character recognition Knowledge transfer Fisher information Dimension reduction and Bregman divergence Artificial intelligence Principal component analysis |
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| References_xml | – ident: ref38 doi: 10.1109/TKDE.2008.212 – ident: ref34 doi: 10.1007/978-3-540-88682-2_55 – ident: ref29 doi: 10.1109/CVPR.2005.170 – ident: ref8 doi: 10.1109/TKDE.2007.190669 – ident: ref6 doi: 10.1023/A:1007379606734 – ident: ref40 doi: 10.1109/ICPR.2008.4760987 – ident: ref1 doi: 10.1145/1273496.1273521 – ident: ref20 doi: 10.1109/TPAMI.2007.1096 – ident: ref25 doi: 10.1145/1015330.1015425 – ident: ref37 doi: 10.1109/CVPR.2009.5206695 – volume: 18 start-page: 1585 volume-title: Advances in Neural Information Processing Systems year: 2006 ident: ref10 article-title: Learning Multiple Related Tasks Using Latent Independent Component Analysis – ident: ref30 doi: 10.1109/34.879790 – start-page: 1671 volume-title: Proc. IEEE Int’l Joint Conf. Neural Networks ident: ref39 article-title: A Unifying Framework for Spectral Analysis Based Dimensionality Reduction – ident: ref21 doi: 10.1109/TKDE.2007.190692 – year: 2006 ident: ref3 article-title: Multi-Task Feature Selection – ident: ref5 doi: 10.1016/B978-1-55860-377-6.50048-7 – start-page: 601 volume-title: Advances in Neural Information Processing Systems year: 2006 ident: ref24 article-title: Correcting Sample Selection Bias by Unlabeled Data – ident: ref15 doi: 10.1109/TPAMI.2005.55 – ident: ref22 doi: 10.1037/h0071325 – ident: ref27 doi: 10.1111/j.1469-1809.1936.tb02137.x – volume: 4 start-page: 1624 year: 1963 ident: ref35 article-title: Regularization of Incorrectly Posed Problems publication-title: Soviet Math Dokl – ident: ref11 doi: 10.1109/TSMCB.2007.911536 – ident: ref17 doi: 10.1145/1273496.1273507 – start-page: 677 volume-title: Proc. 23rd Nat’l Conf. Artificial Intelligence ident: ref14 article-title: Transfer Learning via Dimensionality Reduction – ident: ref26 doi: 10.1109/TPAMI.2007.1037 – volume-title: Proc. Neural Information Processing Systems 2005 Workshop Inductive Transfer: 10 Years Later ident: ref19 article-title: To Transfer or Not to Transfer – start-page: 446 volume-title: Face Recognition: From Theory to Applications year: 1998 ident: ref33 article-title: Characterizing Virtual Eigensignatures for General Purpose Face Recognition doi: 10.1007/978-3-642-72201-1_25 – ident: ref12 doi: 10.1109/TIP.2006.881945 – volume: 16 volume-title: Advances in Neural Information Processing Systems year: 2003 ident: ref28 article-title: Locality Preserving Projections – volume: 19 start-page: 25 volume-title: Advances in Neural Information Processing Systems year: 2006 ident: ref36 article-title: Learning on Graph with Laplacian Regularization – ident: ref31 doi: 10.1162/neco.1995.7.2.219 – volume-title: Proc. AAAI Workshop Transfer Learning for Complex Tasks ident: ref9 article-title: Transfer in Reinforcement Learning via Markov Logic Networks – volume-title: Statistical Learning Theory year: 1998 ident: ref32 – ident: ref7 doi: 10.1109/34.598228 – volume: 3 start-page: 1415 year: 2003 ident: ref16 article-title: Feature Extraction by Non Parametric Mutual Information Maximization publication-title: J. Machine Learning Research – ident: ref2 doi: 10.1109/TPAMI.2008.70 – ident: ref18 doi: 10.1109/ICCV.2007.4408856 – start-page: 299 volume-title: Advances in Neural Information Processing System year: 2006 ident: ref13 article-title: Transfer Learning for Text Classification – ident: ref23 doi: 10.1109/CVPR.2007.383346 – volume: 7 start-page: 2399 year: 2006 ident: ref4 article-title: Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples publication-title: J. Machine Learning Research |
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| SubjectTerms | Algorithm design and analysis Algorithms and Bregman divergence Applied sciences Artificial intelligence Computer science; control theory; systems Data processing. List processing. Character string processing Dimensionality reduction Discriminant analysis Exact sciences and technology Face recognition Hilbert space Independent component analysis Information systems. Data bases Kernel Learning Linear discriminant analysis Memory organisation. Data processing Pattern recognition. Digital image processing. Computational geometry Principal component analysis Regularization Semisupervised learning Software Speech and sound recognition and synthesis. Linguistics Studies Subspaces Testing Text categorization Texts Training |
| Title | Bregman Divergence-Based Regularization for Transfer Subspace Learning |
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