Solving lp-norm regularization with tensor kernels


Saverio Salzo, Lorenzo Rosasco, Johan Suykens ;
Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, PMLR 84:1655-1663, 2018.


In this paper, we discuss how a suitable family of tensor kernels can be used to efficiently solve nonparametric extensions of lp 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.

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