Graph-regularized concept factorization for multi-view document clustering

Authors: Zhan, K., Shi, J., Wang, J. and Tian, F.

Journal: Journal of Visual Communication and Image Representation

Volume: 48

Pages: 411-418

eISSN: 1095-9076

ISSN: 1047-3203

DOI: 10.1016/j.jvcir.2017.02.019

Abstract:

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.

http://eprints.bournemouth.ac.uk/27588/

Source: Scopus

Graph-regularized concept factorization for multi-view document clustering

Authors: Zhan, K., Shi, J., Wang, J. and Tian, F.

Journal: JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION

Volume: 48

Pages: 411-418

eISSN: 1095-9076

ISSN: 1047-3203

DOI: 10.1016/j.jvcir.2017.02.019

http://eprints.bournemouth.ac.uk/27588/

Source: Web of Science (Lite)

Graph-regularized concept factorization for multi-view document clustering

Authors: Zhang, K., Shi, J.H., Wang, J. and Tian, F.

Journal: Journal of Visual Communication and Image Representation

ISSN: 1095-9076

Abstract:

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.

http://eprints.bournemouth.ac.uk/27588/

Source: Manual

Graph-regularized concept factorization for multi-view document clustering.

Authors: Zhang, K., Shi, J.H., Wang, J. and Tian, F.

Journal: Journal of Visual Communication and Image Representation

Volume: 48

Issue: October

Pages: 411-418

ISSN: 1047-3203

Abstract:

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.

http://eprints.bournemouth.ac.uk/27588/

Source: BURO EPrints