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Autore principale: Modell, Alexander
Natura: Preprint
Pubblicazione: 2024
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Accesso online:https://arxiv.org/abs/2405.14494
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author Modell, Alexander
author_facet Modell, Alexander
contents In this paper, we derive entrywise error bounds for low-rank approximations of kernel matrices obtained using the truncated eigen-decomposition (or singular value decomposition). While this approximation is well-known to be optimal with respect to the spectral and Frobenius norm error, little is known about the statistical behaviour of individual entries. Our error bounds fill this gap. A key technical innovation is a delocalisation result for the eigenvectors of the kernel matrix corresponding to small eigenvalues, which takes inspiration from the field of Random Matrix Theory. Finally, we validate our theory with an empirical study of a collection of synthetic and real-world datasets.
format Preprint
id arxiv_https___arxiv_org_abs_2405_14494
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Entrywise error bounds for low-rank approximations of kernel matrices
Modell, Alexander
Statistics Theory
Machine Learning
62G20
In this paper, we derive entrywise error bounds for low-rank approximations of kernel matrices obtained using the truncated eigen-decomposition (or singular value decomposition). While this approximation is well-known to be optimal with respect to the spectral and Frobenius norm error, little is known about the statistical behaviour of individual entries. Our error bounds fill this gap. A key technical innovation is a delocalisation result for the eigenvectors of the kernel matrix corresponding to small eigenvalues, which takes inspiration from the field of Random Matrix Theory. Finally, we validate our theory with an empirical study of a collection of synthetic and real-world datasets.
title Entrywise error bounds for low-rank approximations of kernel matrices
topic Statistics Theory
Machine Learning
62G20
url https://arxiv.org/abs/2405.14494