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Diverse Deep Matrix Factorization with Hypergraph Regularization for Multi-View Data Representation
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DOI：10.1109/JAS.2022.105980.png)
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Deep matrix factorization (DMF) has been demon-strated to be a powerful tool to take in the complex hierarchical information of multi-view data (MDR). 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 multi-view 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 multi-view learning approaches.
Keyword:
Deep matrix factorization (DMF)
diversity
hyper-graph regularization
multi-view data representation (MDR)
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19.2
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1.4K
被引数:
1.1W
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Multi-view manifold regularized learning-based method for prioritizing candidate disease miRNAs
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