  In this paper, we discuss how a suitable family of tensor kernels can be used
to efficiently solve nonparametric extensions of $\ell^p$ regularized learning
methods. Our main contribution is proposing a fast dual algorithm, and showing
that it allows to solve the problem efficiently. Our results contrast recent
findings suggesting kernel methods cannot be extended beyond Hilbert setting.
Numerical experiments confirm the effectiveness of the method.
