Extending Mercer's expansion to indefinite and asymmetric kernels

Sungwoo Jeong, Alex Townsend

Keywords: Kernel methods, Mercer's expansion, Indefinite kernels, Convergence behavior.

TL;DR: We extend and prove new convergence properties of Mercer's expansion for general continuous kernels.

Abstract: Mercer's expansion and Mercer's theorem are cornerstone results in kernel theory. While the classical Mercer's theorem only considers continuous symmetric positive definite kernels, analogous expansions are effective in practice for indefinite and asymmetric kernels. In this paper we extend Mercer's expansion to continuous kernels, providing a rigorous theoretical underpinning for indefinite and asymmetric kernels. We begin by demonstrating that Mercer's expansion may not be pointwise convergent for continuous indefinite kernels, before proving that the expansion of continuous kernels with bounded variation uniformly in each variable separably converges pointwise almost everywhere, almost uniformly, and unconditionally almost everywhere. We also describe an algorithm for computing Mercer's expansion for general kernels and give new decay bounds on its terms.

Primary Area: learning theory

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Submission Number: 3374