Robust Elastic-Net Subspace Representation
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Eunwoo | - |
dc.contributor.author | Lee, Minsik | - |
dc.contributor.author | Oh, Songhwai | - |
dc.date.accessioned | 2021-06-18T08:44:01Z | - |
dc.date.available | 2021-06-18T08:44:01Z | - |
dc.date.issued | 2016-09 | - |
dc.identifier.issn | 1057-7149 | - |
dc.identifier.issn | 1941-0042 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/45663 | - |
dc.description.abstract | Recently, finding the low-dimensional structure of high-dimensional data has gained much attention. Given a set of data points sampled from a single subspace or a union of subspaces, the goal is to learn or capture the underlying subspace structure of the data set. In this paper, we propose elastic-net subspace representation, a new subspace representation framework using elastic-net regularization of singular values. Due to the strong convexity enforced by elastic-net, the proposed method is more stable and robust in the presence of heavy corruptions compared with existing lasso-type rank minimization approaches. For discovering a single low-dimensional subspace, we propose a computationally efficient low-rank factorization algorithm, called FactEN, using a property of the nuclear norm and the augmented Lagrangian method. Then, ClustEN is proposed to handle the general case, in which the data samples are drawn from a union of multiple subspaces, for joint subspace clustering and estimation. The proposed algorithms are applied to a number of subspace representation problems to evaluate the robustness and efficiency under various noisy conditions, and experimental results show the benefits of the proposed method compared with existing methods. | - |
dc.format.extent | 15 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Robust Elastic-Net Subspace Representation | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/TIP.2016.2588321 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON IMAGE PROCESSING, v.25, no.9, pp 4245 - 4259 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000380355600008 | - |
dc.identifier.scopusid | 2-s2.0-84986264820 | - |
dc.citation.endPage | 4259 | - |
dc.citation.number | 9 | - |
dc.citation.startPage | 4245 | - |
dc.citation.title | IEEE TRANSACTIONS ON IMAGE PROCESSING | - |
dc.citation.volume | 25 | - |
dc.type.docType | Article | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | Robust subspace representation | - |
dc.subject.keywordAuthor | elastic-net regularization | - |
dc.subject.keywordAuthor | subspace learning | - |
dc.subject.keywordAuthor | subspace clustering | - |
dc.subject.keywordPlus | RANK MATRIX APPROXIMATIONS | - |
dc.subject.keywordPlus | REGULARIZATION | - |
dc.subject.keywordPlus | FACTORIZATION | - |
dc.subject.keywordPlus | ALGORITHM | - |
dc.subject.keywordPlus | RECOGNITION | - |
dc.subject.keywordPlus | SELECTION | - |
dc.subject.keywordPlus | PURSUIT | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.description.journalRegisteredClass | sci | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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