Multi-label Learning with Label-Specific Features via Clustering Ensemble

Multi-label learning deals with objects with rich semantics where each example is associated with multiple class labels simultaneously. Intuitively, each class label is supposed to possess specific characteristics of its own. Therefore, exploiting label-specific features serves as one of the promising techniques to learn from multi-label examples. Specifically, the LIFT approach generates the label-specific features by clustering the multi-label training examples in a label-wise style, which ignores the utilization of label correlations to improve generalization performance. In this paper, a new multi-label learning method named LIFTACE (multi-label learning with Label-specIfic FeaTures viA Clustering Ensemble) is proposed, which generates label-specific features by considering label correlations via clustering ensemble techniques. Extensive experimental results show that, LIFTACE can achieve better generalization performance than LIFT by exploiting label correlations in label-specific features generation.