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LogDet Rank Minimization with Application to Subspace Clustering
Joint Authors
Kang, Zhao
Peng, Chong
Cheng, Jie
Cheng, Qiang
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-07-02
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Low-rank matrix is desired in many machine learning and computer vision problems.
Most of the recent studies use the nuclear norm as a convex surrogate of the rank operator.
However, all singular values are simply added together by the nuclear norm, and thus the rank may not be well approximated in practical problems.
In this paper, we propose using a log-determinant (LogDet) function as a smooth and closer, though nonconvex, approximation to rank for obtaining a low-rank representation in subspace clustering.
Augmented Lagrange multipliers strategy is applied to iteratively optimize the LogDet-based nonconvex objective function on potentially large-scale data.
By making use of the angular information of principal directions of the resultant low-rank representation, an affinity graph matrix is constructed for spectral clustering.
Experimental results on motion segmentation and face clustering data demonstrate that the proposed method often outperforms state-of-the-art subspace clustering algorithms.
American Psychological Association (APA)
Kang, Zhao& Peng, Chong& Cheng, Jie& Cheng, Qiang. 2015. LogDet Rank Minimization with Application to Subspace Clustering. Computational Intelligence and Neuroscience،Vol. 2015, no. 2015, pp.1-10.
https://search.emarefa.net/detail/BIM-1057762
Modern Language Association (MLA)
Kang, Zhao…[et al.]. LogDet Rank Minimization with Application to Subspace Clustering. Computational Intelligence and Neuroscience No. 2015 (2015), pp.1-10.
https://search.emarefa.net/detail/BIM-1057762
American Medical Association (AMA)
Kang, Zhao& Peng, Chong& Cheng, Jie& Cheng, Qiang. LogDet Rank Minimization with Application to Subspace Clustering. Computational Intelligence and Neuroscience. 2015. Vol. 2015, no. 2015, pp.1-10.
https://search.emarefa.net/detail/BIM-1057762
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1057762