The Gaussian kernel on the circle and spaces that admit isometric
embeddings of the circle
- URL: http://arxiv.org/abs/2302.10623v1
- Date: Tue, 21 Feb 2023 12:17:14 GMT
- Title: The Gaussian kernel on the circle and spaces that admit isometric
embeddings of the circle
- Authors: Natha\"el Da Costa, Cyrus Mostajeran, Juan-Pablo Ortega
- Abstract summary: 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.
- Score: 4.576379639081977
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- Variable Hyperparameterized Gaussian Kernel using Displaced Squeezed Vacuum State [2.1408617023874443]
A multimode coherent state can generate the Gaussian kernel with a constant value of hyper parameter.
This constant hyper parameter has limited the application of the Gaussian kernel when it is applied to complex learning problems.
We realize the variable hyper parameterized kernel with a multimode-displaced squeezed vacuum state.
arXiv Detail & Related papers (2024-03-18T08:25:56Z) - Invariant kernels on Riemannian symmetric spaces: a harmonic-analytic approach [6.5497574505866885]
This work aims to prove that the classical Gaussian kernel, when defined on a non-Euclidean symmetric space, is never positive-definite for any choice of parameter.
New results lay out a blueprint for the study of invariant kernels on symmetric spaces.
arXiv Detail & Related papers (2023-10-30T05:06:52Z) - Geometric Learning with Positively Decomposable Kernels [6.5497574505866885]
We propose the use of reproducing kernel Krein space (RKKS) based methods, which require only kernels that admit a positive decomposition.
We show that one does not need to access this decomposition in order to learn in RKKS.
arXiv Detail & Related papers (2023-10-20T21:18:04Z) - A Nearly Tight Bound for Fitting an Ellipsoid to Gaussian Random Points [50.90125395570797]
This nearly establishes a conjecture ofciteSaundersonCPW12, within logarithmic factors.
The latter conjecture has attracted significant attention over the past decade, due to its connections to machine learning and sum-of-squares lower bounds for certain statistical problems.
arXiv Detail & Related papers (2022-12-21T17:48:01Z) - Sobolev Spaces, Kernels and Discrepancies over Hyperspheres [4.521119623956821]
This work provides theoretical foundations for kernel methods in the hyperspherical context.
We characterise the native spaces (reproducing kernel Hilbert spaces) and the Sobolev spaces associated with kernels defined over hyperspheres.
Our results have direct consequences for kernel cubature, determining the rate of convergence of the worst case error, and expanding the applicability of cubature algorithms.
arXiv Detail & Related papers (2022-11-16T20:31:38Z) - Geometry-aware Bayesian Optimization in Robotics using Riemannian
Mat\'ern Kernels [64.62221198500467]
We show how to implement geometry-aware kernels for Bayesian optimization.
This technique can be used for control parameter tuning, parametric policy adaptation, and structure design in robotics.
arXiv Detail & Related papers (2021-11-02T09:47:22Z) - Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge
Equivariant Projected Kernels [108.60991563944351]
We present a recipe for constructing gauge equivariant kernels, which induce vector-valued Gaussian processes coherent with geometry.
We extend standard Gaussian process training methods, such as variational inference, to this setting.
arXiv Detail & Related papers (2021-10-27T13:31:10Z) - A Note on Optimizing Distributions using Kernel Mean Embeddings [94.96262888797257]
Kernel mean embeddings represent probability measures by their infinite-dimensional mean embeddings in a reproducing kernel Hilbert space.
We show that when the kernel is characteristic, distributions with a kernel sum-of-squares density are dense.
We provide algorithms to optimize such distributions in the finite-sample setting.
arXiv Detail & Related papers (2021-06-18T08:33:45Z) - Taming Nonconvexity in Kernel Feature Selection---Favorable Properties
of the Laplace Kernel [77.73399781313893]
A challenge is to establish the objective function of kernel-based feature selection.
The gradient-based algorithms available for non-global optimization are only able to guarantee convergence to local minima.
arXiv Detail & Related papers (2021-06-17T11:05:48Z) - Strong Uniform Consistency with Rates for Kernel Density Estimators with
General Kernels on Manifolds [11.927892660941643]
We show how to handle kernel density estimation with intricate kernels not designed by the user.
The isotropic kernels considered in this paper are different from the kernels in the Vapnik-Chervonenkis class that are frequently considered in statistics society.
arXiv Detail & Related papers (2020-07-13T14:36:06Z) - Metrizing Weak Convergence with Maximum Mean Discrepancies [88.54422104669078]
This paper characterizes the maximum mean discrepancies (MMD) that metrize the weak convergence of probability measures for a wide class of kernels.
We prove that, on a locally compact, non-compact, Hausdorff space, the MMD of a bounded continuous Borel measurable kernel k, metrizes the weak convergence of probability measures if and only if k is continuous.
arXiv Detail & Related papers (2020-06-16T15:49:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.