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

Zhang, K., Shi, J.H., Wang, J. and Tian, F., 2017. Graph-regularized concept factorization for multi-view document clustering. Journal of Visual Communication and Image Representation, 48 (October), 411-418.

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JVCI-16-575.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.


DOI: 10.1016/j.jvcir.2017.02.019


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.

Item Type:Article
Uncontrolled Keywords:Multi-view learning; Concept factorization; Document clustering; Manifold learning
Group:Faculty of Science & Technology
ID Code:27588
Deposited By: Symplectic RT2
Deposited On:02 Mar 2017 14:31
Last Modified:14 Mar 2022 14:03


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