Elastic-net regularization of singular values for robust subspace learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Eunwoo | - |
dc.contributor.author | Lee, Minsik | - |
dc.contributor.author | Oh, Songhwai | - |
dc.date.accessioned | 2021-06-22T21:25:13Z | - |
dc.date.available | 2021-06-22T21:25:13Z | - |
dc.date.created | 2021-01-22 | - |
dc.date.issued | 2015-10 | - |
dc.identifier.issn | 1063-6919 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/20244 | - |
dc.description.abstract | Learning a low-dimensional structure plays an important role in computer vision. Recently, a new family of methods, such as l1 minimization and robust principal component analysis, has been proposed for low-rank matrix approximation problems and shown to be robust against outliers and missing data. But these methods often require heavy computational load and can fail to find a solution when highly corrupted data are presented. In this paper, an elastic-net regularization based low-rank matrix factorization method for subspace learning is proposed. The proposed method finds a robust solution efficiently by enforcing a strong convex constraint to improve the algorithm's stability while maintaining the low-rank property of the solution. It is shown that any stationary point of the proposed algorithm satisfies the Karush-Kuhn-Tucker optimality conditions. The proposed method is applied to a number of low-rank matrix approximation problems to demonstrate its efficiency in the presence of heavy corruptions and to show its effectiveness and robustness compared to the existing methods. © 2015 IEEE. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE Computer Society | - |
dc.title | Elastic-net regularization of singular values for robust subspace learning | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Minsik | - |
dc.identifier.doi | 10.1109/CVPR.2015.7298693 | - |
dc.identifier.scopusid | 2-s2.0-84959200618 | - |
dc.identifier.wosid | 000387959200100 | - |
dc.identifier.bibliographicCitation | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, v.07-12-June-2015, pp.915 - 923 | - |
dc.relation.isPartOf | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | - |
dc.citation.title | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | - |
dc.citation.volume | 07-12-June-2015 | - |
dc.citation.startPage | 915 | - |
dc.citation.endPage | 923 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.subject.keywordPlus | RANK MATRIX APPROXIMATIONS | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/7298693 | - |
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