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- Publisher Website: 10.1109/TSMCB.2010.2085433
- Scopus: eid_2-s2.0-79957454703
- PMID: 21118781
- WOS: WOS:000290734400006
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Article: Semisupervised dimensionality reduction and classification through virtual label regression
Title | Semisupervised dimensionality reduction and classification through virtual label regression |
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Authors | |
Keywords | Dimensionality reduction label propagation label regression semisupervised learning subspace learning |
Issue Date | 2011 |
Citation | IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2011, v. 41, n. 3, p. 675-685 How to Cite? |
Abstract | Semisupervised dimensionality reduction has been attracting much attention as it not only utilizes both labeled and unlabeled data simultaneously, but also works well in the situation of out-of-sample. This paper proposes an effective approach of semisupervised dimensionality reduction through label propagation and label regression. Different from previous efforts, the new approach propagates the label information from labeled to unlabeled data with a well-designed mechanism of random walks, in which outliers are effectively detected and the obtained virtual labels of unlabeled data can be well encoded in a weighted regression model. These virtual labels are thereafter regressed with a linear model to calculate the projection matrix for dimensionality reduction. By this means, when the manifold or the clustering assumption of data is satisfied, the labels of labeled data can be correctly propagated to the unlabeled data; and thus, the proposed approach utilizes the labeled and the unlabeled data more effectively than previous work. Experimental results are carried out upon several databases, and the advantage of the new approach is well demonstrated. © 2010 IEEE. |
Persistent Identifier | http://hdl.handle.net/10722/321442 |
ISSN | 2014 Impact Factor: 6.220 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Nie, Feiping | - |
dc.contributor.author | Xu, Dong | - |
dc.contributor.author | Li, Xuelong | - |
dc.contributor.author | Xiang, Shiming | - |
dc.date.accessioned | 2022-11-03T02:18:57Z | - |
dc.date.available | 2022-11-03T02:18:57Z | - |
dc.date.issued | 2011 | - |
dc.identifier.citation | IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2011, v. 41, n. 3, p. 675-685 | - |
dc.identifier.issn | 1083-4419 | - |
dc.identifier.uri | http://hdl.handle.net/10722/321442 | - |
dc.description.abstract | Semisupervised dimensionality reduction has been attracting much attention as it not only utilizes both labeled and unlabeled data simultaneously, but also works well in the situation of out-of-sample. This paper proposes an effective approach of semisupervised dimensionality reduction through label propagation and label regression. Different from previous efforts, the new approach propagates the label information from labeled to unlabeled data with a well-designed mechanism of random walks, in which outliers are effectively detected and the obtained virtual labels of unlabeled data can be well encoded in a weighted regression model. These virtual labels are thereafter regressed with a linear model to calculate the projection matrix for dimensionality reduction. By this means, when the manifold or the clustering assumption of data is satisfied, the labels of labeled data can be correctly propagated to the unlabeled data; and thus, the proposed approach utilizes the labeled and the unlabeled data more effectively than previous work. Experimental results are carried out upon several databases, and the advantage of the new approach is well demonstrated. © 2010 IEEE. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics | - |
dc.subject | Dimensionality reduction | - |
dc.subject | label propagation | - |
dc.subject | label regression | - |
dc.subject | semisupervised learning | - |
dc.subject | subspace learning | - |
dc.title | Semisupervised dimensionality reduction and classification through virtual label regression | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TSMCB.2010.2085433 | - |
dc.identifier.pmid | 21118781 | - |
dc.identifier.scopus | eid_2-s2.0-79957454703 | - |
dc.identifier.volume | 41 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 675 | - |
dc.identifier.epage | 685 | - |
dc.identifier.isi | WOS:000290734400006 | - |