Gaussian kernel quadrature at scaled Gauss-Hermite nodes

TL;DR

This paper presents a precise, stable method for computing kernel quadrature weights using scaled Gauss-Hermite nodes for Gaussian kernels, with proven exponential convergence for functions in the associated RKHS.

Contribution

It introduces an explicit, numerically stable approximation to kernel quadrature weights on Gaussian measure using scaled Gauss-Hermite nodes and Mercer eigendecomposition truncation.

Findings

Kernel quadrature weights are positive.

The approximation achieves exponential convergence.

Numerical results confirm stability and accuracy.

Abstract

This article derives an accurate, explicit, and numerically stable approximation to the kernel quadrature weights in one dimension and on tensor product grids when the kernel and integration measure are Gaussian. The approximation is based on use of scaled Gauss-Hermite nodes and truncation of the Mercer eigendecomposition of the Gaussian kernel. Numerical evidence indicates that both the kernel quadrature and the approximate weights at these nodes are positive. An exponential rate of convergence for functions in the reproducing kernel Hilbert space induced by the Gaussian kernel is proved under an assumption on growth of the sum of absolute values of the approximate weights.