Robust Dual-Graph Regularized Deep Matrix Factorization for Multi-view Clustering

Zhenqiu Shu, Bin Li, Cong Hu, Zhengtao Yu, Xiaojun Wu

Abstract: The matrix factorization approaches have been widely applied for multi-view clustering since they can effectively explore complementary information contained in the multi-view data. However, some prior knowledge hidden in multi-view data cannot be fully exploited in existing matrix factorization based multi-view learning approaches. In this paper, we present a robust dual-graph regularized deep matrix factorization (RDDMF) approach for multi-view clustering. Specifically, it integrates the dual-graph regularizers and the sparse constraints into the deep matrix factorization framework. Therefore, the proposed RDDMF approach discovers the geometric structures of both the data and the feature space by adding the dual graph regularization term into deep matrix factorization in each layer. Meanwhile, the sparse constraints are imposed on the coefficient matrix of each layer to improve the robustness of our model. Besides, we design an efficient optimization strategy of the proposed model and give its convergence rate. Numerous experiments on four well-known datasets show our proposed RDDMF approach is superior to several state-of-the-art approaches in multi-view clustering.