Localized bases for kernel spaces on the unit sphere

TL;DR

This paper develops computationally efficient, localized basis functions for kernel spaces on the sphere, enabling scalable approximation and interpolation with large scattered data sets, and validates the approach through numerical experiments.

Contribution

It introduces well-localized, small-footprint basis functions for kernel spaces on the sphere, with theoretical stability and decay properties, and demonstrates their effectiveness through large-scale numerical experiments.

Findings

Basis elements use only O((log N)^2) kernels, reducing computational cost.

The new basis is stable in L_p spaces and exhibits polynomial decay.

Numerical experiments validate the theory on datasets with over 150,000 points.

Abstract

Approximation/interpolation from spaces of positive definite or conditionally positive definite kernels is an increasingly popular tool for the analysis and synthesis of scattered data, and is central to many meshless methods. For a set of $N$ scattered sites, the standard basis for such a space utilizes $N$ \emph{globally} supported kernels; computing with it is prohibitively expensive for large $N$. Easily computable, well-localized bases, with "small-footprint" basis elements - i.e., elements using only a small number of kernels -- have been unavailable. Working on $\sphere$, with focus on the restricted surface spline kernels (e.g. the thin-plate splines restricted to the sphere), we construct easily computable, spatially well-localized, small-footprint, robust bases for the associated kernel spaces. Our theory predicts that each element of the local basis is constructed by using a combination of only $\mathcal{O}((\log N)^2)$ kernels, which makes the construction computationally cheap. We prove that the new basis is $L_p$ stable and satisfies polynomial decay estimates that are stationary with respect to the density of the data sites, and we present a quasi-interpolation scheme that provides optimal $L_p$ approximation orders. Although our focus is on $\mathbb{S}^2$, much of the theory applies to other manifolds - $\mathbb{S}^d$, the rotation group, and so on. Finally, we construct algorithms to implement these schemes and use them to conduct numerical experiments, which validate our theory for interpolation problems on $\mathbb{S}^2$ involving over one hundred fifty thousand data sites.