Multi-Kernel Multi-Label Learning with Max-Margin Concept Network
Wei Zhang, Xiangyang Xue, Jianping Fan, Xiaojing Huang, Bin Wu, Mingjie Liu
In this paper, a novel method is developed for enabling Multi-Kernel Multi-Label Learning. Inter-label dependency and similarity diversity are simultaneously leveraged in the proposed method. A concept network is constructed to capture the inter-label correlations for classifier training. Maximal margin approach is used to effectively formulate the feature-label associations and the label-label correlations. Specific kernels are learned not only for each label but also for each pair of the inter-related labels. By learning the eigenfunctions of the kernels, the similarity between a new data point and the training samples can be computed in the online mode. Our experimental results on real datasets (web pages, images, music, and bioinformatics) have demonstrated the effectiveness of our method.