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.
https://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
https://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.
https://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.
https://eprints.bournemouth.ac.uk/27588/
Source: BURO EPrints