Constrained Low-Rank Representation for Robust Subspace Clustering

Authors: Wang, J., Wang, X., Tian, F., Liu, C.H. and Yu, H.

http://eprints.bournemouth.ac.uk/24966/

Journal: IEEE Transactions on Cybernetics

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

ISSN: 2168-2275

DOI: 10.1109/TCYB.2016.2618852

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.

This data was imported from Scopus:

Authors: Wang, J., Wang, X., Tian, F., Liu, C.H. and Yu, H.

http://eprints.bournemouth.ac.uk/24966/

Journal: IEEE Transactions on Cybernetics

Volume: 47

Issue: 12

Pages: 4534-4546

ISSN: 2168-2267

DOI: 10.1109/TCYB.2016.2618852

© 2016 IEEE. Subspace clustering aims to partition the data points drawn from a union of subspaces according to their underlying 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 subspace 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 semisupervised 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.

This data was imported from Web of Science (Lite):

Authors: Wang, J., Wang, X., Tian, F., Liu, C.H. and Yu, H.

http://eprints.bournemouth.ac.uk/24966/

Journal: IEEE TRANSACTIONS ON CYBERNETICS

Volume: 47

Issue: 12

Pages: 4534-4546

eISSN: 2168-2275

ISSN: 2168-2267

DOI: 10.1109/TCYB.2016.2618852

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