Constrained Low-Rank Representation for Robust Subspace Clustering.

Wang, J., Wang, X., Tian, F., Liu, C. H. and Yu, H., 2016. Constrained Low-Rank Representation for Robust Subspace Clustering. IEEE Transactions on Cybernetics. (In Press)

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DOI: 10.1109/TCYB.2016.2618852

Abstract

Subspace clustering aims to partition the data points drawn from a union of subspaces according to their underly- ing subspaces. For accurate semisupervised subspace clustering,all data that have a must-link constraint or the same label should be grouped into the same underlying subspace. However, this is not guaranteed in existing approaches. Moreover, these approaches require additional parameters for incorporating supervision information. In this paper, we propose a constrained low-rank representation (CLRR) for robust semisupervised sub-space clustering, based on a novel constraint matrix constructed in this paper. While seeking the low-rank representation of data, CLRR explicitly incorporates supervision information as hard constraints for enhancing the discriminating power of optimal representation. This strategy can be further extended to other state-of-the-art methods, such as sparse subspace clustering. We theoretically prove that the optimal representation matrix has both a block-diagonal structure with clean data and a semisu-pervised grouping effect with noisy data. We have also developed an efficient optimization algorithm based on alternating the direction method of multipliers for CLRR. Our experimental results have demonstrated that CLRR outperforms existing methods.

Item Type:Article
ISSN:2168-2275
Additional Information:Copyright 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Uncontrolled Keywords:Low-rank representation (LRR); semisupervised learning; subspace clustering
Subjects:UNSPECIFIED
Group:Faculty of Science & Technology
ID Code:24966
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:17 Nov 2016 10:19
Last Modified:17 Nov 2016 10:19

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