PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning
Marius Kloft and Gilles Blanchard
Arxiv preprint Number 1103.0790v1, 2011.


We derive an upper bound on the local Rademacher complexity of $\ell_p$-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 $1\leq p\leq\infty$, 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 $O(n^{-\frac{\alpha}{1+\alpha}})$, where $\alpha$ is the minimum eigenvalue decay rate of the individual kernels.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:8085
Deposited By:Marius Kloft
Deposited On:18 April 2011