Elastic-net regularization of singular values for robust subspace learning
- Authors
- Kim, Eunwoo; Lee, Minsik; Oh, Songhwai
- Issue Date
- Oct-2015
- Publisher
- IEEE Computer Society
- Citation
- Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, v.07-12-June-2015, pp.915 - 923
- Indexed
- SCIE
SCOPUS
- Journal Title
- Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
- Volume
- 07-12-June-2015
- Start Page
- 915
- End Page
- 923
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/20244
- DOI
- 10.1109/CVPR.2015.7298693
- ISSN
- 1063-6919
- 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.
- Files in This Item
-
Go to Link
- Appears in
Collections - COLLEGE OF ENGINEERING SCIENCES > SCHOOL OF ELECTRICAL ENGINEERING > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.