Iterative views agreement: an iterative low-rank based structured optimization method to multi-view spectral clustering

Yang Wang, Zhang Wenjie, Lin Wu, Xuemin Lin, Meng Fang, Shirui Pan

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Multi-view spectral clustering, which aims at yielding an agreement or consensus data objects grouping across multi-views with their graph laplacian matrices, is a fundamental clustering problem. Among the existing methods, Low-Rank Representation (LRR) based method is quite superior in terms of its effectiveness, intuitiveness and robustness to noise corruptions. However, it aggressively tries to learn a common low-dimensional subspace for multi-view data, while inattentively ignoring the local manifold structure in each view, which is critically important to the spectral clustering; worse still, the low-rank minimization is enforced to achieve the data correlation consensus among all views, failing to flexibly preserve the local manifold structure for each view. In this paper, 1) we propose a multi-graph laplacian regularized LRR with each graph laplacian corresponding to one view to characterize its local manifold structure. 2) Instead of directly enforcing the low-rank minimization among all views for correlation consensus, we separately impose low-rank constraint on each view, coupled with a mutual structural consensus constraint, where it is able to not only well preserve the local manifold structure but also serve as a constraint for that from other views, which iteratively makes the views more agreeable. Extensive experiments on real-world multi-view data sets demonstrate its superiority.

Original languageEnglish
Title of host publicationIJCAI-16 - Proceedings of the 25th International Joint Conference on Artificial Intelligence, IJCAI 2016
Subtitle of host publicationNew York, New York, USA 9–15 July 2016
EditorsSubbarao Kambhampati
Place of PublicationPalo Alto CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Pages2153-2159
Number of pages7
ISBN (Electronic)9781577357704
Publication statusPublished - 2016
Externally publishedYes
EventInternational Joint Conference on Artificial Intelligence 2016 - New York, United States of America
Duration: 9 Jul 201615 Jul 2016
Conference number: 25th
http://ijcai-16.org/

Conference

ConferenceInternational Joint Conference on Artificial Intelligence 2016
Abbreviated titleIJCAI 2016
CountryUnited States of America
CityNew York
Period9/07/1615/07/16
Internet address

Cite this

Wang, Y., Wenjie, Z., Wu, L., Lin, X., Fang, M., & Pan, S. (2016). Iterative views agreement: an iterative low-rank based structured optimization method to multi-view spectral clustering. In S. Kambhampati (Ed.), IJCAI-16 - Proceedings of the 25th International Joint Conference on Artificial Intelligence, IJCAI 2016: New York, New York, USA 9–15 July 2016 (pp. 2153-2159). Palo Alto CA USA: Association for the Advancement of Artificial Intelligence (AAAI).
Wang, Yang ; Wenjie, Zhang ; Wu, Lin ; Lin, Xuemin ; Fang, Meng ; Pan, Shirui. / Iterative views agreement : an iterative low-rank based structured optimization method to multi-view spectral clustering. IJCAI-16 - Proceedings of the 25th International Joint Conference on Artificial Intelligence, IJCAI 2016: New York, New York, USA 9–15 July 2016. editor / Subbarao Kambhampati. Palo Alto CA USA : Association for the Advancement of Artificial Intelligence (AAAI), 2016. pp. 2153-2159
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title = "Iterative views agreement: an iterative low-rank based structured optimization method to multi-view spectral clustering",
abstract = "Multi-view spectral clustering, which aims at yielding an agreement or consensus data objects grouping across multi-views with their graph laplacian matrices, is a fundamental clustering problem. Among the existing methods, Low-Rank Representation (LRR) based method is quite superior in terms of its effectiveness, intuitiveness and robustness to noise corruptions. However, it aggressively tries to learn a common low-dimensional subspace for multi-view data, while inattentively ignoring the local manifold structure in each view, which is critically important to the spectral clustering; worse still, the low-rank minimization is enforced to achieve the data correlation consensus among all views, failing to flexibly preserve the local manifold structure for each view. In this paper, 1) we propose a multi-graph laplacian regularized LRR with each graph laplacian corresponding to one view to characterize its local manifold structure. 2) Instead of directly enforcing the low-rank minimization among all views for correlation consensus, we separately impose low-rank constraint on each view, coupled with a mutual structural consensus constraint, where it is able to not only well preserve the local manifold structure but also serve as a constraint for that from other views, which iteratively makes the views more agreeable. Extensive experiments on real-world multi-view data sets demonstrate its superiority.",
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language = "English",
pages = "2153--2159",
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Wang, Y, Wenjie, Z, Wu, L, Lin, X, Fang, M & Pan, S 2016, Iterative views agreement: an iterative low-rank based structured optimization method to multi-view spectral clustering. in S Kambhampati (ed.), IJCAI-16 - Proceedings of the 25th International Joint Conference on Artificial Intelligence, IJCAI 2016: New York, New York, USA 9–15 July 2016. Association for the Advancement of Artificial Intelligence (AAAI), Palo Alto CA USA, pp. 2153-2159, International Joint Conference on Artificial Intelligence 2016, New York, United States of America, 9/07/16.

Iterative views agreement : an iterative low-rank based structured optimization method to multi-view spectral clustering. / Wang, Yang; Wenjie, Zhang; Wu, Lin; Lin, Xuemin; Fang, Meng; Pan, Shirui.

IJCAI-16 - Proceedings of the 25th International Joint Conference on Artificial Intelligence, IJCAI 2016: New York, New York, USA 9–15 July 2016. ed. / Subbarao Kambhampati. Palo Alto CA USA : Association for the Advancement of Artificial Intelligence (AAAI), 2016. p. 2153-2159.

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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AU - Pan, Shirui

PY - 2016

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N2 - Multi-view spectral clustering, which aims at yielding an agreement or consensus data objects grouping across multi-views with their graph laplacian matrices, is a fundamental clustering problem. Among the existing methods, Low-Rank Representation (LRR) based method is quite superior in terms of its effectiveness, intuitiveness and robustness to noise corruptions. However, it aggressively tries to learn a common low-dimensional subspace for multi-view data, while inattentively ignoring the local manifold structure in each view, which is critically important to the spectral clustering; worse still, the low-rank minimization is enforced to achieve the data correlation consensus among all views, failing to flexibly preserve the local manifold structure for each view. In this paper, 1) we propose a multi-graph laplacian regularized LRR with each graph laplacian corresponding to one view to characterize its local manifold structure. 2) Instead of directly enforcing the low-rank minimization among all views for correlation consensus, we separately impose low-rank constraint on each view, coupled with a mutual structural consensus constraint, where it is able to not only well preserve the local manifold structure but also serve as a constraint for that from other views, which iteratively makes the views more agreeable. Extensive experiments on real-world multi-view data sets demonstrate its superiority.

AB - Multi-view spectral clustering, which aims at yielding an agreement or consensus data objects grouping across multi-views with their graph laplacian matrices, is a fundamental clustering problem. Among the existing methods, Low-Rank Representation (LRR) based method is quite superior in terms of its effectiveness, intuitiveness and robustness to noise corruptions. However, it aggressively tries to learn a common low-dimensional subspace for multi-view data, while inattentively ignoring the local manifold structure in each view, which is critically important to the spectral clustering; worse still, the low-rank minimization is enforced to achieve the data correlation consensus among all views, failing to flexibly preserve the local manifold structure for each view. In this paper, 1) we propose a multi-graph laplacian regularized LRR with each graph laplacian corresponding to one view to characterize its local manifold structure. 2) Instead of directly enforcing the low-rank minimization among all views for correlation consensus, we separately impose low-rank constraint on each view, coupled with a mutual structural consensus constraint, where it is able to not only well preserve the local manifold structure but also serve as a constraint for that from other views, which iteratively makes the views more agreeable. Extensive experiments on real-world multi-view data sets demonstrate its superiority.

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CY - Palo Alto CA USA

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Wang Y, Wenjie Z, Wu L, Lin X, Fang M, Pan S. Iterative views agreement: an iterative low-rank based structured optimization method to multi-view spectral clustering. In Kambhampati S, editor, IJCAI-16 - Proceedings of the 25th International Joint Conference on Artificial Intelligence, IJCAI 2016: New York, New York, USA 9–15 July 2016. Palo Alto CA USA: Association for the Advancement of Artificial Intelligence (AAAI). 2016. p. 2153-2159