Localized Multiple Kernel Learning—A Convex Approach


Yunwen Lei, Alexander Binder, Urun Dogan, Marius Kloft ;
Proceedings of The 8th Asian Conference on Machine Learning, PMLR 63:81-96, 2016.


We propose a localized approach to multiple kernel learning that can be formulated as a convex optimization problem over a given cluster structure. For which we obtain generalization error guarantees and derive an optimization algorithm based on the Fenchel dual representation. Experiments on real-world datasets from the application domains of computational biology and computer vision show that convex localized multiple kernel learning can achieve higher prediction accuracies than its global and non-convex local counterparts.

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