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2020

Diversity sampling is an implicit regularization for kernel methods

Fanuel, Michaël, Schreurs, Joachim, Suykens, Johan A. K.

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Kernel methods have achieved very good performance on large scale regression and classification problems, by using the Nystr\"om method and preconditioning techniques.

The Nystr\"om approximation -- based on a subset of landmarks -- gives a low rank approximation of the kernel matrix, and is known to provide a form of implicit regularization.
We further elaborate on the impact of sampling diverse landmarks for constructing the Nystr\"om approximation in supervised as well as unsupervised kernel methods.
By using Determinantal Point Processes for sampling, we obtain additional theoretical results concerning the interplay between diversity and regularization.

Diversity sampling is an implicit regularization for kernel methods · Around

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