AISTATS 2018poster0 citations
Solving lp-norm regularization with tensor kernels
Saverio Salzo, Lorenzo Rosasco, Johan Suykens
Abstract
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.
BibTeX
@InProceedings{pmlr-v84-salzo18a,
title = {Solving lp-norm regularization with tensor kernels},
author = {Salzo, Saverio and Rosasco, Lorenzo and Suykens, Johan},
booktitle = {Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics},
pages = {1655--1663},
year = {2018},
editor = {Storkey, Amos and Perez-Cruz, Fernando},
volume = {84},
series = {Proceedings of Machine Learning Research},
month = {09--11 Apr},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v84/salzo18a/salzo18a.pdf},
url = {https://proceedings.mlr.press/v84/salzo18a.html},
abstract = {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. }
}