A sublinear-time randomized algorithm for column and row subset selection based on strong rank-revealing QR factorizations
Numerical Analysis
2024-02-22 v1 Numerical Analysis
Abstract
In this work, we analyze a sublinear-time algorithm for selecting a few rows and columns of a matrix for low-rank approximation purposes. The algorithm is based on an initial uniformly random selection of rows and columns, followed by a refinement of this choice using a strong rank-revealing QR factorization. We prove bounds on the error of the corresponding low-rank approximation (more precisely, the CUR approximation error) when the matrix is a perturbation of a low-rank matrix that can be factorized into the product of matrices with suitable incoherence and/or sparsity assumptions.
Cite
@article{arxiv.2402.13975,
title = {A sublinear-time randomized algorithm for column and row subset selection based on strong rank-revealing QR factorizations},
author = {Alice Cortinovis and Lexing Ying},
journal= {arXiv preprint arXiv:2402.13975},
year = {2024}
}