DocumentCode :
76856
Title :
Robust Subspace Segmentation Via Low-Rank Representation
Author :
Jinhui Chen ; Jian Yang
Author_Institution :
Dept. of Comput. Sci., Nanjing Univ. of Sci. & Technol., Nanjing, China
Volume :
44
Issue :
8
fYear :
2014
fDate :
Aug. 2014
Firstpage :
1432
Lastpage :
1445
Abstract :
Recently the low-rank representation (LRR) has been successfully used in exploring the multiple subspace structures of data. It assumes that the observed data is drawn from several low-rank subspaces and sometimes contaminated by outliers and occlusions. However, the noise (low-rank representation residual) is assumed to be sparse, which is generally characterized by minimizing the l1-norm of the residual. This actually assumes that the residual follows the Laplacian distribution. The Laplacian assumption, however, may not be accurate enough to describe various noises in real scenarios. In this paper, we propose a new framework, termed robust low-rank representation, by considering the low-rank representation as a low-rank constrained estimation for the errors in the observed data. This framework aims to find the maximum likelihood estimation solution of the low-rank representation residuals. We present an efficient iteratively reweighted inexact augmented Lagrange multiplier algorithm to solve the new problem. Extensive experimental results show that our framework is more robust to various noises (illumination, occlusion, etc) than LRR, and also outperforms other state-of-the-art methods.
Keywords :
computer vision; data structures; image representation; image segmentation; iterative methods; maximum likelihood estimation; pattern clustering; statistical distributions; Laplacian assumption; Laplacian distribution; iteratively reweighted inexact augmented Lagrange multiplier algorithm; low-rank constrained estimation; low-rank representation residual; maximum likelihood estimation solution; multiple subspace data structures; robust low-rank representation; robust subspace segmentation; Algorithm design and analysis; Dictionaries; Laplace equations; Maximum likelihood estimation; Noise; Optimization; Robustness; Low-rank representation; matrix recovery; robust regression; subspace segmentation;
fLanguage :
English
Journal_Title :
Cybernetics, IEEE Transactions on
Publisher :
ieee
ISSN :
2168-2267
Type :
jour
DOI :
10.1109/TCYB.2013.2286106
Filename :
6651776
Link To Document :
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