Robust Mapping Learning for Multi-view Multi-label Classification with Missing Labels

Abstract

The multi-label classification problem has generated significant interest in recent years. Typical scenarios assume each instance can be assigned to a set of labels. Most of previous works regard the original labels as authentic label assignments which ignore missing labels in realistic applications. Meanwhile, few studies handle the data coming from multiple sources (multiple views) to enhance label correlations. In this paper, we propose a new robust method for multi-label classification problem. The proposed method incorporates multiple views into a mixed feature matrix, and augments the initial label matrix with label correlation matrix to estimate authentic label assignments. In addition, a low-rank structure and a manifold regularization are used to further exploit global label correlations and local smoothness. An alternating algorithm is designed to slove the optimization problem. Experiments on three authoritative datasets demonstrate the effectiveness and robustness of our method.