Robust orthogonal matrix factorization for efficient subspace learning
- Authors
- Kim, Eunwoo; Oh, Songhwai
- Issue Date
- Nov-2015
- Publisher
- ELSEVIER SCIENCE BV
- Keywords
- Low-rank matrix factorization; l(1)-norm; Subspace learning; Augmented Lagrangian method; Rank estimation
- Citation
- NEUROCOMPUTING, v.167, pp 218 - 229
- Pages
- 12
- Journal Title
- NEUROCOMPUTING
- Volume
- 167
- Start Page
- 218
- End Page
- 229
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/45722
- DOI
- 10.1016/j.neucom.2015.04.074
- ISSN
- 0925-2312
1872-8286
- Abstract
- Low-rank matrix factorization plays an important role in the areas of pattern recognition, computer vision, and machine learning. Recently, a new family of methods, such as l(1)-norm minimization and robust PCA, has been proposed for low-rank subspace analysis problems and has shown to be robust against outliers and missing data. But these methods suffer from heavy computation loads and can fail to find a solution when highly corrupted data are presented. In this paper, a robust orthogonal matrix approximation method using fixed-rank factorization is proposed. The proposed method finds a robust solution efficiently using orthogonality and smoothness constraints. The proposed method is also extended to handle the rank uncertainty issue by a rank estimation strategy for practical real-world problems. The proposed method is applied to a number of low-rank matrix approximation problems and experimental results show that the proposed method is highly accurate, fast, and efficient compared to the existing methods. (C) 2015 Elsevier B.V. All rights reserved.
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Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
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