The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning

03/03/2011

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by   Marius Kloft, et al.

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We derive an upper bound on the local Rademacher complexity of ℓ_p-norm multiple kernel learning, which yields a tighter excess risk bound than global approaches. Previous local approaches aimed at analyzed the case p=1 only while our analysis covers all cases 1≤ p≤∞, assuming the different feature mappings corresponding to the different kernels to be uncorrelated. We also show a lower bound that shows that the bound is tight, and derive consequences regarding excess loss, namely fast convergence rates of the order O(n^-α/1+α), where α is the minimum eigenvalue decay rate of the individual kernels.