Semi-Supervised Multi-view Multi-label Classification Based on Nonnegative Matrix Factorization

Many real-world applications involve multi-label classification where each sample is usually associated with a set of labels. Although many methods have been proposed, most of them are just applicable to single-view data neglecting the complementary information among multiple views. Besides, most existing methods are supervised, hence they cannot handle the case where only a few labeled data are available. To address these issues, we propose a novel semi-supervised multi-view multi-label classification method based on nonnegative matrix factorization (NMF). Specifically, it explores the complementary information by adopting multi-view NMF, regularizes the learned labels of each view towards a common consensus labeling, and obtains the labels of the unlabeled data guided by supervised information. Experimental results on real-world benchmark datasets demonstrate the superior performance of our method over the state-of-the-art methods.