Multi-Label Learning with Regularization Enriched Label-Specific Features

Multi-label learning learns from examples each associated with multiple class labels simultaneously, and the goal is to induce a predictive model which can assign a set of relevant labels for the unseen instance. Label-specific features serve as an effective strategy towards inducing multi-label predictive model, where the relevancy of each class label is determined by employing tailored features encoding inherent and distinct characteristics of the class label its own. In this paper, a regularization based approach named REEL is proposed for label-specific features generation, which works by enriching label-specific feature representation for each class label via synergizing informative label-specific features from other class labels with sparse regularization. Specifically, full-order label correlations are considered by REEL while the number of classifiers induced for multi-label prediction is linear to the number of class labels. Extensive experiments on fifteen benchmark multi-label data sets clearly show the favorable performance of REEL against other state-of-the-art multi-label learning approaches with label-specific features.