Graph-regularized concept factorization for multi-view document clustering

Zhan, K; Shi, J; Wang, J; Tian, F

Graph-regularized concept factorization for multi-view document clustering

Zhan, K

Shi, J Wang, J

Tian, F

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Publication Type:: Journal Article
Citation:: Journal of Visual Communication and Image Representation, 2017, 48 pp. 411 - 418
Issue Date:: 2017-10-01

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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 https://orcid.org/0000-0002-8000-5682	en_US
dc.contributor.author	Tian, F	en_US
dc.date.issued	2017-10-01	en_US
dc.identifier.citation	Journal of Visual Communication and Image Representation, 2017, 48 pp. 411 - 418	en_US
dc.identifier.issn	1047-3203	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125846
dc.description.abstract	© 2017 Elsevier Inc. We propose a novel multi-view document clustering method with the graph-regularized concept factorization (MVCF). MVCF makes full use of multi-view features for more comprehensive understanding of the data and learns weights for each view adaptively. It also preserves the local geometrical structure of the manifolds for multi-view clustering. We have derived an efficient optimization algorithm to solve the objective function of MVCF and proven its convergence by utilizing the auxiliary function method. Experiments carried out on three benchmark datasets have demonstrated the effectiveness of MVCF in comparison to several state-of-the-art approaches in terms of accuracy, normalized mutual information and purity.	en_US
dc.relation.ispartof	Journal of Visual Communication and Image Representation	en_US
dc.relation.isbasedon	10.1016/j.jvcir.2017.02.019	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Graph-regularized concept factorization for multi-view document clustering	en_US
dc.type	Journal Article
utslib.citation.volume	48	en_US
utslib.for	1905 Visual Arts and Crafts	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1203 Design Practice and Management	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.publication-status	Published	en_US
pubs.volume	48	en_US

Abstract:

© 2017 Elsevier Inc. We propose a novel multi-view document clustering method with the graph-regularized concept factorization (MVCF). MVCF makes full use of multi-view features for more comprehensive understanding of the data and learns weights for each view adaptively. It also preserves the local geometrical structure of the manifolds for multi-view clustering. We have derived an efficient optimization algorithm to solve the objective function of MVCF and proven its convergence by utilizing the auxiliary function method. Experiments carried out on three benchmark datasets have demonstrated the effectiveness of MVCF in comparison to several state-of-the-art approaches in terms of accuracy, normalized mutual information and purity.

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http://hdl.handle.net/10453/125846