Multi-view Graph Regularized Deep Autoencoder-like NMF Framework

Many real-world data are composed of different representations or views, thus multi-view clustering (MVC) has attracted more and more attention in recent years. Its key task is how to extract sufficient fusion features from multi-view data. Because the nonnegative matrix factorization (NMF) can favorably explain the extracted features, the NMF based MVC is usually a good choice for multi-view data, and promising results are achieved. Inspired by this, we propose a multi-view graph regularized deep autoencoder-like NMF (MGANMF) framework in this paper for multi-view clustering. MGANMF uses the deep autoencoder-like NMF, which draws lessons from the idea of depth automatic encoder, to learn the hierarchical semantics of multi-view data in a layer-wise manner. Moreover, in order to describe the inherent geometric structure in each view data, graph regulators are introduced to couple the output representation of deep structure. In addition, the self-updating weights are employed to balance the effect of each view. Thus, a new objective function is defined and the optimization processes are presented. Experimental results on several multi-view datasets show the effectiveness of the proposed model.