  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.
