Localized Multiple Kernel Learning---A Convex Approach

Yunwen Lei; Alexander Binder; Ürün Dogan; Marius Kloft

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arXiv · 1506.04364

Localized Multiple Kernel Learning---A Convex Approach

Yunwen Lei·Alexander Binder·Ürün Dogan·Marius Kloft

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Abstract

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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Yunwen Lei, Alexander Binder, Ürün Dogan, Marius Kloft. 2015-06-14. Localized Multiple Kernel Learning---A Convex Approach. https://arxiv.org/abs/1506.04364

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