Mutual information based multi-label feature selection via constrained convex optimization

Abstract Multi-label learning has been extensively studied in many areas such as information retrieval, bioinformatics, and multimedia annotation. However, multi-label datasets often have noisy, irrelevant and redundant features with high dimensionality. Accompanying with these issues, a critical challenge is known as the curse of dimensionality. As an effective data preprocessing method, feature selection has received much attention for that it can provide a way in reducing computation time, improving prediction performance and enhancing understanding of the data. Based on this, a large number of information-theoretical-based feature selection methods are developed to solve the learning problem, i.e. multi-label classification. Unfortunately, most of existing information-theoretical-based feature selection methods are either directly transformed from single-label feature selection methods or insufficient in light of using heuristic algorithms as the search component. Motivated by this, in this paper, we propose a novel mutual-information-based feature selection method, which obtains the optimal solution via constrained convex optimization with less time. Specifically, by incorporating the label information into the feature selection process, label-correlation is taken into consideration to generate the generalized model. Finally, the experimental results on various multi-label datasets demonstrate the effectiveness and efficiency of the proposed framework.