Computer Science > Machine Learning
[Submitted on 30 Oct 2023 (v1), last revised 6 Sep 2024 (this version, v2)]
Title:Invariant kernels on Riemannian symmetric spaces: a harmonic-analytic approach
View PDF HTML (experimental)Abstract: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. To achieve this goal, the paper develops new geometric and analytical arguments. These provide a rigorous characterization of the positive-definiteness of the Gaussian kernel, which is complete but for a limited number of scenarios in low dimensions that are treated by numerical computations. Chief among these results are the L$^{\!\scriptscriptstyle p}$-$\hspace{0.02cm}$Godement theorems (where $p = 1,2$), which provide verifiable necessary and sufficient conditions for a kernel defined on a symmetric space of non-compact type to be positive-definite. A celebrated theorem, sometimes called the Bochner-Godement theorem, already gives such conditions and is far more general in its scope, but is especially hard to apply. Beyond the connection with the Gaussian kernel, the new results in this work lay out a blueprint for the study of invariant kernels on symmetric spaces, bringing forth specific harmonic analysis tools that suggest many future applications.
Submission history
From: Cyrus Mostajeran Dr [view email][v1] Mon, 30 Oct 2023 05:06:52 UTC (561 KB)
[v2] Fri, 6 Sep 2024 05:45:42 UTC (318 KB)
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