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Constrained low-rank representation for robust subspace clustering

Constrained low-rank representation for robust subspace clustering

Wang, Jing, Wang, Xiao, Tian, Feng, Liu, Chang Hong and Yu, Hongchuan (2016) Constrained low-rank representation for robust subspace clustering. IEEE Transactions on Cybernetics, 47 (12). pp. 4534-4546. ISSN 2168-2267 (Print), 2168-2275 (Online) (doi:

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Subspace clustering aims to partition the data points drawn from a union of subspaces according to their underlying subspaces. For accurate semi-supervised 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 semi-supervised 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 semi-supervised 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
Uncontrolled Keywords: low rank representation, subspace clustering, semi-supervised learning
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > School of Computing & Mathematical Sciences (CAM)
Last Modified: 15 Dec 2020 16:28
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None

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