On the Convergence Rate of lp-Norm Multiple Kernel Learning
Marius Kloft, Gilles Blanchard; 13(80):2465−2502, 2012.
Abstract
We derive an upper bound on the local Rademacher complexity of lp-norm multiple kernel learning, which yields a tighter excess risk bound than global approaches. Previous local approaches 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.
[abs]
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