Works (1)
Updated: July 5th, 2023 15:30
2019 journal article
LOW-RANK MATRIX APPROXIMATIONS DO NOT NEED A SINGULAR VALUE GAP
SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 40(1), 299–319.
Contributors: * & I. Ipsen n
author keywords: singular value decomposition; principal angles; additive perturbations; multiplicative perturbations
TL;DR:
It is shown that the low-rank approximation errors, in the two-norm, Frobenius norm and more generally, any Schatten p- norm, are insensitive to additive rank-preserving perturbations in the projector basis; and to matrix perturbation that are additive or change the number of columns.
(via Semantic Scholar)
open access via arxiv.org (repository)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: April 9, 2019
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