  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.
