The Gaussian kernel on the circle and spaces that admit isometric embeddings of the circle
Not on the map. Why?
Abstract (arXiv)
On Euclidean spaces, the Gaussian kernel is one of the most widely used kernels in applications. It has also been used on non-Euclidean spaces, where it is known that there may be (and often are) scale parameters for which it is not positive definite. Hope remains that this kernel is positive definite for many choices of parameter. However, we show that the Gaussian kernel is not positive definite on the circle for any choice of parameter. This implies that on metric spaces in which the circle can be isometrically embedded, such as spheres, projective spaces and Grassmannians, the Gaussian kernel is not positive definite for any parameter.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 8 of its 26 references that are in the map. First posted on arXiv 21 Feb 2023.
Builds on
8 of 26 references.
References · 8
- 2020Mat'ern Gaussian processes on Riemannian manifoldscited by 82
- 2020The Signature Kernel is the solution of a Goursat PDEcited by 58
- 2014Geodesic Exponential Kernels: When Curvature and Linearity Conflictcited by 47
- 2014Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernelscited by 30
- 2019Bayesian Optimization Meets Riemannian Manifolds in Robot Learningcited by 23
- 2019Gaussians on Riemannian Manifolds: Applications for Robot Learning and Adaptive Controlcited by 17
- 2020Dimension reduction in recurrent networks by canonicalizationcited by 11
- 2022Reservoir kernels and Volterra seriescited by 8
Built on by
Citing papers per year: 2023: 3; 2024: 0; 2025: 1; 2026: 1.
5 papers.
- 2023Invariant kernels on Riemannian symmetric spaces: a harmonic-analytic approachcited by 6
- 2023Gaussian kernels on non-simply-connected closed Riemannian manifolds are never positive definitecited by 3
- 2023Geometric Learning with Positively Decomposable Kernelscited by 3
- 2025Memory Capacity of Nonlinear Recurrent Networks: Is it Informative?cited by 3
- 2026RW-Flow: One-Step Generation on Compact Manifolds via Wasserstein Gradient Flowscited by 0
- 2026RW-Flow: One-Step Generation on Compact Manifolds via Wasserstein Gradient Flows30 Sep 2026
- 2025Memory Capacity of Nonlinear Recurrent Networks: Is it Informative?7 Feb 2025
- 2023Invariant kernels on Riemannian symmetric spaces: a harmonic-analytic approach30 Oct 2023
- 2023Geometric Learning with Positively Decomposable Kernels20 Oct 2023
- 2023Gaussian kernels on non-simply-connected closed Riemannian manifolds are never positive definite12 Mar 2023