Discriminatively Constrained Semi-supervised Multi-view Nonnegative Matrix Factorization with Graph Regularization

• URL: http://arxiv.org/abs/2010.13297v1
• Date: Mon, 26 Oct 2020 02:58:11 GMT
• Title: Discriminatively Constrained Semi-supervised Multi-view Nonnegative Matrix Factorization with Graph Regularization
• Authors: Guosheng Cui, Ruxin Wang, Dan Wu, and Ye Li
• Abstract summary: We propose a novel Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization (DCS2MVNMF) Specifically, a discriminative weighting matrix is introduced for the auxiliary matrix of each view, which enhances the inter-class distinction. In addition, we design a new feature scale normalization strategy to align the multiple views and complete the corresponding iterative optimization schemes.
• Score: 10.978930376656423
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, semi-supervised multi-view nonnegative matrix factorization (MVNMF) algorithms have achieved promising performances for multi-view clustering. While most of semi-supervised MVNMFs have failed to effectively consider discriminative information among clusters and feature alignment from multiple views simultaneously. In this paper, a novel Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization (DCS^2MVNMF) is proposed. Specifically, a discriminative weighting matrix is introduced for the auxiliary matrix of each view, which enhances the inter-class distinction. Meanwhile, a new graph regularization is constructed with the label and geometrical information. In addition, we design a new feature scale normalization strategy to align the multiple views and complete the corresponding iterative optimization schemes. Extensive experiments conducted on several real world multi-view datasets have demonstrated the effectiveness of the proposed method.

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