Less is More: Nyström Computational Regularization

TitleLess is More: Nyström Computational Regularization
Publication TypeConference Paper
Year of Publication2015
AuthorsRudi, A, Camoriano, R, Rosasco, L
Conference NameNIPS 2015
Other NumbersarXiv:1507.04717v6

We study Nystr"om type subsampling approaches to large scale kernel methods, and prove learning bounds in the statistical learning setting, where random sampling and high probability estimates are considered. In particular, we prove that these approaches can achieve optimal learning bounds, provided the subsampling level is suitably chosen. These results suggest a simple incremental variant of Nystr"om Kernel Regularized Least Squares, where the subsampling level implements a form of computational regularization, in the sense that it controls at the same time regularization and computations. Extensive experimental analysis shows that the considered approach achieves state of the art performances on benchmark large scale datasets.


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