Multi-view clustering via label-embedded regularized NMF with dual-graph constraints

Abstract

Nonnegative matrix factorization (NMF) methods have achieved remarkable performances in multi-view clustering due to their effectiveness and efficiency. To better obtain a low-dimensional common representation, the limited labels and the geometric structure of the multi-view data should be fully utilized in clustering. In this work, we introduce a novel multi-view learning approach, dubbed label-embedded regularized NMF with dual-graph constraints (LeNMF-DC), for clustering. Our proposed LeNMF-DC approach mainly utilizes matrix factorization to obtain a low-dimensional common representation of the multi-view data, in which the prior knowledge hidden in data can be fully explored. Specifically, we construct three graph regularization terms to preserve the manifold structure in the data, feature and label space, respectively. Moreover, we take advantage of the labels of the labeled samples without additional parameters. In addition, we develop an alternate iterative optimization scheme to solve the model of LeNMF-DC and then show its convergence rate. Compared with traditional multi-view clustering approaches, the labels of unlabeled samples in our proposed LeNMF-DC approach are assigned by the label constraint matrix rather than the clustering algorithm, and thus it avoids performance loss during the clustering. Experimental results on four benchmark datasets manifest that our LeNMF-DC approach can achieve superior performances than several state-of-the-art approaches in multi-view clustering.