Robust Elastic-Net Subspace Representation
- Authors
- Kim, Eunwoo; Lee, Minsik; Oh, Songhwai
- Issue Date
- Sep-2016
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Robust subspace representation; elastic-net regularization; subspace learning; subspace clustering
- Citation
- IEEE TRANSACTIONS ON IMAGE PROCESSING, v.25, no.9, pp.4245 - 4259
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE TRANSACTIONS ON IMAGE PROCESSING
- Volume
- 25
- Number
- 9
- Start Page
- 4245
- End Page
- 4259
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/13066
- DOI
- 10.1109/TIP.2016.2588321
- ISSN
- 1057-7149
- 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.
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