Joint multi-label classification and label correlations with missing labels and feature selection.

Multi-label classification problem is a key learning task where each instance may belong to multiple class labels simultaneously. However, there exists four main challenges: (a) designing an effective multi-label classifier, (b) learning the high-order asymmetric label correlations automatically, (c) reducing the dimensionality of feature space, (d) dealing with both the full labels and missing labels cases. In this paper, we directly address the above four problems in a unified learning framework, and propose a novel Multi-Label classification approach joint with label correlations, Missing labels and Feature selection, named MLMF. The proposed MLMF not only makes the joint learning of independent binary classifiers, but also allows the joint learning of multi-label classification and label correlations. Meanwhile, the shared sparse feature structure among labels are selected by l2,1-norm. Furthermore, MLMF can also handle missing labels. Experimental results on sixteen multi-label data sets in terms of six evaluation criteria demonstrate that MLMF outperforms the state-of-the-art multi-label classification algorithms.