Spectral alignment of kernel matrices and applications

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Hauptverfasser: Wenzel, Tizan, Iske, Armin
Format: Preprint
Veröffentlicht: 2024
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author Wenzel, Tizan
Iske, Armin
author_facet Wenzel, Tizan
Iske, Armin
contents Kernel matrices are a key quantity in kernel-based approximation, and important properties such as stability and algorithmic convergence can be analyzed with their help. In this work we refine a multivariate Ingham-type theorem, which is then leveraged to obtain novel and refined stability estimates on kernel matrices. For this, we focus on the case of finitely smooth kernels, such as the family of Matérn or Wendland kernels, while noting that the results also extend to norm-equivalent kernels. In particular we obtain results that relate the Rayleigh quotients of kernel matrices for kernels of different smoothness to each other. Finally we comment on conclusions for the eigenvectors of these kernel matrices.
format Preprint
id arxiv_https___arxiv_org_abs_2409_04263
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Spectral alignment of kernel matrices and applications
Wenzel, Tizan
Iske, Armin
Numerical Analysis
Kernel matrices are a key quantity in kernel-based approximation, and important properties such as stability and algorithmic convergence can be analyzed with their help. In this work we refine a multivariate Ingham-type theorem, which is then leveraged to obtain novel and refined stability estimates on kernel matrices. For this, we focus on the case of finitely smooth kernels, such as the family of Matérn or Wendland kernels, while noting that the results also extend to norm-equivalent kernels. In particular we obtain results that relate the Rayleigh quotients of kernel matrices for kernels of different smoothness to each other. Finally we comment on conclusions for the eigenvectors of these kernel matrices.
title Spectral alignment of kernel matrices and applications
topic Numerical Analysis
url https://arxiv.org/abs/2409.04263