Title: Quadrature-based features for kernel approximation,
Abstract: We consider the problem of improving kernel approximation via randomized
feature maps. These maps arise as Monte Carlo approximation to integral
representations of kernel functions and scale up kernel methods for larger
datasets. Based on an efficient numerical integration technique, we propose a
unifying approach that reinterprets the previous random features methods and
extends to better estimates of the kernel approximation. We derive the
convergence behaviour and conduct an extensive empirical study that supports
our hypothesis.