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- Publisher Website: 10.1109/TNNLS.2011.2178037
- Scopus: eid_2-s2.0-84867796463
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Article: Semi-supervised dimension reduction using trace ratio criterion
Title | Semi-supervised dimension reduction using trace ratio criterion |
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Authors | |
Keywords | Flexible semi-supervised discriminant analysis semi-supervised dimension reduction trace ratio |
Issue Date | 2012 |
Citation | IEEE Transactions on Neural Networks and Learning Systems, 2012, v. 23, n. 3, p. 519-526 How to Cite? |
Abstract | In this brief, we address the trace ratio (TR) problem for semi-supervised dimension reduction. We first reformulate the objective function of the recent work semi-supervised discriminant analysis (SDA) in a TR form. We also observe that in SDA the low-dimensional data representation F is constrained to be in the linear subspace spanned by the training data matrix X (i.e., F = X T W). In order to relax this hard constraint, we introduce a flexible regularizer ||F-XT W||2 which models the regression residual into the reformulated objective function. With such relaxation, our method referred to as TR based flexible SDA (TR-FSDA) can better cope with data sampled from a certain type of nonlinear manifold that is somewhat close to a linear subspace. In order to address the non-trivial optimization problem in TR-FSDA, we further develop an iterative algorithm to simultaneously solve for the low-dimensional data representation F and the projection matrix W. Moreover, we theoretically prove that our iterative algorithm converges to the optimum based on the Newton-Raphson method. The experiments on two face databases, one shape image database and one webpage database demonstrate that TR-FSDA outperforms the existing semi-supervised dimension reduction methods. © 2012 IEEE. |
Persistent Identifier | http://hdl.handle.net/10722/321491 |
ISSN | 2023 Impact Factor: 10.2 2023 SCImago Journal Rankings: 4.170 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Huang, Yi | - |
dc.contributor.author | Xu, Dong | - |
dc.contributor.author | Nie, Feiping | - |
dc.date.accessioned | 2022-11-03T02:19:16Z | - |
dc.date.available | 2022-11-03T02:19:16Z | - |
dc.date.issued | 2012 | - |
dc.identifier.citation | IEEE Transactions on Neural Networks and Learning Systems, 2012, v. 23, n. 3, p. 519-526 | - |
dc.identifier.issn | 2162-237X | - |
dc.identifier.uri | http://hdl.handle.net/10722/321491 | - |
dc.description.abstract | In this brief, we address the trace ratio (TR) problem for semi-supervised dimension reduction. We first reformulate the objective function of the recent work semi-supervised discriminant analysis (SDA) in a TR form. We also observe that in SDA the low-dimensional data representation F is constrained to be in the linear subspace spanned by the training data matrix X (i.e., F = X T W). In order to relax this hard constraint, we introduce a flexible regularizer ||F-XT W||2 which models the regression residual into the reformulated objective function. With such relaxation, our method referred to as TR based flexible SDA (TR-FSDA) can better cope with data sampled from a certain type of nonlinear manifold that is somewhat close to a linear subspace. In order to address the non-trivial optimization problem in TR-FSDA, we further develop an iterative algorithm to simultaneously solve for the low-dimensional data representation F and the projection matrix W. Moreover, we theoretically prove that our iterative algorithm converges to the optimum based on the Newton-Raphson method. The experiments on two face databases, one shape image database and one webpage database demonstrate that TR-FSDA outperforms the existing semi-supervised dimension reduction methods. © 2012 IEEE. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Neural Networks and Learning Systems | - |
dc.subject | Flexible semi-supervised discriminant analysis | - |
dc.subject | semi-supervised dimension reduction | - |
dc.subject | trace ratio | - |
dc.title | Semi-supervised dimension reduction using trace ratio criterion | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TNNLS.2011.2178037 | - |
dc.identifier.scopus | eid_2-s2.0-84867796463 | - |
dc.identifier.volume | 23 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 519 | - |
dc.identifier.epage | 526 | - |
dc.identifier.eissn | 2162-2388 | - |
dc.identifier.isi | WOS:000302705100012 | - |