On the Convergence Rate of -Norm Multiple Kernel Learning

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] [ pdf ][ bib ] © JMLR 2012. (edit, beta)