Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이민식 | - |
dc.date.accessioned | 2018-04-19T07:39:00Z | - |
dc.date.available | 2018-04-19T07:39:00Z | - |
dc.date.issued | 2016-09 | - |
dc.identifier.citation | IEEE TRANSACTIONS ON IMAGE PROCESSING, v. 25, No. 9, Page. 4245-4259 | en_US |
dc.identifier.issn | 1057-7149 | - |
dc.identifier.issn | 1941-0042 | - |
dc.identifier.uri | https://ieeexplore.ieee.org/abstract/document/7506231/ | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/69477 | - |
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. | en_US |
dc.description.sponsorship | This work was supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2013R1A1A2065551) and in part by the Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (B0101-16-0307, Basic Software Research in Human-Level Lifelong Machine Learning). The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Keigo Hirakawa. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | en_US |
dc.subject | Robust subspace representation | en_US |
dc.subject | elastic-net regularization | en_US |
dc.subject | subspace learning | en_US |
dc.subject | subspace clustering | en_US |
dc.subject | PRINCIPAL COMPONENT ANALYSIS | en_US |
dc.subject | RANK MATRIX APPROXIMATIONS | en_US |
dc.subject | REGULARIZATION ALGORITHMS | en_US |
dc.subject | LEAST-SQUARES | en_US |
dc.subject | MISSING DATA | en_US |
dc.subject | L-1 NORM | en_US |
dc.subject | FACTORIZATION | en_US |
dc.subject | RECOGNITION | en_US |
dc.subject | SELECTION | en_US |
dc.title | Robust Elastic-Net Subspace Representation | en_US |
dc.type | Article | en_US |
dc.relation.no | 9 | - |
dc.relation.volume | 25 | - |
dc.identifier.doi | 10.1109/TIP.2016.2588321 | - |
dc.relation.page | 4245-4259 | - |
dc.relation.journal | IEEE TRANSACTIONS ON IMAGE PROCESSING | - |
dc.contributor.googleauthor | Kim, E | - |
dc.contributor.googleauthor | Lee, M | - |
dc.contributor.googleauthor | Oh, S | - |
dc.relation.code | 2016000231 | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF ENGINEERING SCIENCES[E] | - |
dc.sector.department | DIVISION OF ELECTRICAL ENGINEERING | - |
dc.identifier.pid | mleepaper | - |
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