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 bioinfor-matics) have demonstrated the effectiveness of our method.
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