Group-preserving label-specific feature selection for multi-label learning

In many real-world application domains, e.g., text categorization and image annotation, objects naturally belong to more than one class label, giving rise to the multi-label learning problem. The performance of multi-label learning greatly relies on the quality of available features, whereas the data generally involve a lot of irrelevant, redundant, even noisy features. This fact has led to that a surge of research on feature selection methods that select significant features for multi-label learning. Nevertheless, most of the previous approaches suffer from the deficiency that label-specific features are not taken into account, and they are also inefficient in exploiting labeling information such as local label correlations. Moreover, these methods lack interpretability, which can only find a feature subset for all labels, however, cannot show how features are related to different labels. Based on this, we present a new group-preserving label-specific feature selection (GLFS) framework for multi-label learning, which simultaneously considers the features special to the labels in the same group and specific features owned by each label to execute feature selection. In addition, we further consider to learn label-group and instance-group correlations for the exploitation of labeling information, and make a collaborative use of them to improve the model generalization. Extensive experiments validate the advantages of the proposed GLFS method.