Low Rank Multi-Label Classification with Missing Labels

Abstract

Multi-label classification has attracted significant interests in various domains. In many applications, only partial labels are available and the others are missing or not provided. How to design an accurate multi-label classifier with such partial labeled data is a challenging problem. In this paper, we propose a Low Rank multi-label classification with Missing Label method (LRML), which joints label matrix recovery and multi-label classifier learning to address the classification problem. The proposed algorithm recover the missing labels via laplacian manifold regularization derived from the feature space. By utilizing the low-rank mapping, the proposed algorithm can efficiently exploit the label correlations and analyze the high-dimensional data in the discriminant subspace simultaneously. Besides, the proposed algorithm is formulated as a convex but not smooth optimization problem. An effective algorithm which divides the problem into multiple convex and smooth sub-problems is developed, together with some theoretical analyses. Experimental results validate that our method leads to a significant improvement in performance and robustness to missing labels over other well-established algorithms.