RFFNet: Large-Scale Interpretable Kernel Methods via Random Fourier Features

arXiv:2211.06410v2 Announce Type: replace-cross
Abstract: Kernel methods provide a flexible and theoretically grounded approach to nonlinear and nonparametric learning. While memory and run-time requirements hinder their applicability to large datasets, many low-rank kernel approximations, such as random Fourier features, were recently developed to scale up such kernel methods. However, these scalable approaches are based on approximations of isotropic kernels, which cannot remove the influence of irrelevant features. In this work, we design random Fourier features for a family of automatic …

abstract arxiv cs.lg datasets features fourier hinder however kernel large datasets low memory random requirements scalable scale stat.ml type via