Key points are not available for this paper at this time.
Deep Matrix Factorization (DMF) has been demonstrated to be a powerful tool to take in the complex hierarchical information of multi-view data. However, existing multi-view DMF methods mainly explore the consistency of multi-view data, while neglecting the diversity among different views as well as the high-order relationships of data, resulting in the loss of valuable complementary information. In this paper, we design a Hypergraph regularized Diverse Deep Matrix Factorization (HDDMF) model for multi-view data representation, to jointly utilize multiview diversity and a high-order manifold in a multi-layer factorization framework. A novel diversity enhancement term is designed to exploit the structural complementarity between different views of data. Hypergraph regularization is utilized to preserve the high-order geometry structure of data in each view. An efficient iterative optimization algorithm is developed to solve the proposed model with theoretical convergence analysis. Experimental results on five real-world data sets demonstrate that the proposed method significantly outperforms state-of-the-art multiview learning approaches.
Huang et al. (Tue,) studied this question.