Abstract In this paper, a novel method is developed for enabling Multi-Kernel Multi-Label Learning. Interlabel dependency and similarity diversity are simultaneously leveraged in the proposed method. A concept network is constructed to capture the inter-label correlations for classi?er training. Maximal margin approach is used to effectively formulate the feature-label associations and the labellabel correlations. Speci?c 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.