Constrained Low-Rank Representation for Robust Subspace Clustering
Authors: Wang, J., Wang, X., Tian, F., Liu, C.H. and Yu, H.
Journal: IEEE Transactions on Cybernetics
Volume: 47
Issue: 12
Pages: 4534-4546
ISSN: 2168-2267
DOI: 10.1109/TCYB.2016.2618852
Abstract: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.
https://eprints.bournemouth.ac.uk/24966/
Source: Scopus
Constrained Low-Rank Representation for Robust Subspace Clustering.
Authors: Wang, J., Wang, X., Tian, F., Liu, C.H. and Yu, H.
Journal: IEEE Trans Cybern
Volume: 47
Issue: 12
Pages: 4534-4546
eISSN: 2168-2275
DOI: 10.1109/TCYB.2016.2618852
Abstract: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.
https://eprints.bournemouth.ac.uk/24966/
Source: PubMed
Constrained Low-Rank Representation for Robust Subspace Clustering
Authors: Wang, J., Wang, X., Tian, F., Liu, C.H. and Yu, H.
Journal: IEEE TRANSACTIONS ON CYBERNETICS
Volume: 47
Issue: 12
Pages: 4534-4546
eISSN: 2168-2275
ISSN: 2168-2267
DOI: 10.1109/TCYB.2016.2618852
https://eprints.bournemouth.ac.uk/24966/
Source: Web of Science (Lite)
Constrained Low-Rank Representation for Robust Subspace Clustering
Authors: Wang, J., Wang, X., Tian, F., Liu, C.H. and Yu, H.
Journal: IEEE Transactions on Cybernetics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
ISSN: 2168-2275
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.
https://eprints.bournemouth.ac.uk/24966/
Source: Manual
Constrained Low-Rank Representation for Robust Subspace Clustering.
Authors: Wang, J., Wang, X., Tian, F., Liu, C.H. and Yu, H.
Journal: IEEE transactions on cybernetics
Volume: 47
Issue: 12
Pages: 4534-4546
eISSN: 2168-2275
ISSN: 2168-2267
DOI: 10.1109/tcyb.2016.2618852
Abstract: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.
https://eprints.bournemouth.ac.uk/24966/
Source: Europe PubMed Central
Constrained Low-Rank Representation for Robust Subspace Clustering
Authors: Wang, J., Wang, X., Tian, F., Liu, C.H. and Yu, H.
Journal: IEEE Transactions on Cybernetics
Volume: 47
Issue: 12
Pages: 4534-4546
ISSN: 2168-2275
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.
https://eprints.bournemouth.ac.uk/24966/
Source: BURO EPrints