Adaptive structure concept factorization for multiview clustering

Zhan, K; Shi, J; Wang, J; Wang, H; Xie, Y

Adaptive structure concept factorization for multiview clustering

Zhan, K

Shi, J Wang, J Wang, H Xie, Y

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Publication Type:: Journal Article
Citation:: Neural Computation, 2018, 30 (4), pp. 1080 - 1103
Issue Date:: 2018-04-01

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Field	Value	Language
dc.contributor.author	Zhan, K https://orcid.org/0000-0002-8000-5682	en_US
dc.contributor.author	Shi, J	en_US
dc.contributor.author	Wang, J	en_US
dc.contributor.author	Wang, H	en_US
dc.contributor.author	Xie, Y	en_US
dc.date.issued	2018-04-01	en_US
dc.identifier.citation	Neural Computation, 2018, 30 (4), pp. 1080 - 1103	en_US
dc.identifier.issn	0899-7667	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132765
dc.description.abstract	© 2018 Massachusetts Institute of Technology. Most existing multiview clustering methods require that graph matrices in different views are computed beforehand and that each graph is obtained independently. However, this requirement ignores the correlation between multiple views. In this letter, we tackle the problem of multiview clustering by jointly optimizing the graph matrix to make full use of the data correlation between views.With the interview correlation, a concept factorization-based multiview clustering method is developed for data integration, and the adaptive method correlates the affinity weights of all views. This method differs from nonnegative matrix factorization- based clustering methods in that it can be applicable to data sets containing negative values. Experiments are conducted to demonstrate the effectiveness of the proposed method in comparison with state-of-theart approaches in terms of accuracy, normalized mutual information, and purity.	en_US
dc.relation.ispartof	Neural Computation	en_US
dc.relation.isbasedon	10.1162/NECO_a_01055	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Adaptive structure concept factorization for multiview clustering	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	30	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	30	en_US

Abstract:

© 2018 Massachusetts Institute of Technology. Most existing multiview clustering methods require that graph matrices in different views are computed beforehand and that each graph is obtained independently. However, this requirement ignores the correlation between multiple views. In this letter, we tackle the problem of multiview clustering by jointly optimizing the graph matrix to make full use of the data correlation between views.With the interview correlation, a concept factorization-based multiview clustering method is developed for data integration, and the adaptive method correlates the affinity weights of all views. This method differs from nonnegative matrix factorization- based clustering methods in that it can be applicable to data sets containing negative values. Experiments are conducted to demonstrate the effectiveness of the proposed method in comparison with state-of-theart approaches in terms of accuracy, normalized mutual information, and purity.

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http://hdl.handle.net/10453/132765