Incorporating multiple cluster centers for multi-label learning

Multi-label learning deals with the problem that each instance is associated multiple labels simultaneously. Most of existing approaches aim to improve performance multi-label by exploiting label correlations. Although data augmentation technique widely used in many machine tasks, it still unclear whether helpful learning. In this article, we propose leverage Specifically, first a novel approach performs clustering on real examples and treats cluster centers as virtual examples, these naturally embody local correlations importances. Then, motivated assumption same should have label, regularization term bridge gap between which can promote smoothness function. Extensive experimental results number real-world datasets clearly demonstrate our proposed outperforms state-of-the-art counterparts.