A PTAS for $\ell_p$-Low Rank Approximation
Data Structures and Algorithms
2021-02-09 v3 Computational Complexity
Machine Learning
Abstract
A number of recent works have studied algorithms for entrywise -low rank approximation, namely, algorithms which given an matrix (with ), output a rank- matrix minimizing when ; and for . On the algorithmic side, for , we give the first -approximation algorithm running in time . Further, for , we give the first almost-linear time approximation scheme for what we call the Generalized Binary -Rank- problem. Our algorithm computes -approximation in time . On the hardness of approximation side, for , assuming the Small Set Expansion Hypothesis and the Exponential Time Hypothesis (ETH), we show that there exists such that the entrywise -Rank- problem has no -approximation algorithm running in time .
Cite
@article{arxiv.1807.06101,
title = {A PTAS for $\ell_p$-Low Rank Approximation},
author = {Frank Ban and Vijay Bhattiprolu and Karl Bringmann and Pavel Kolev and Euiwoong Lee and David P. Woodruff},
journal= {arXiv preprint arXiv:1807.06101},
year = {2021}
}
Comments
Accepted at SODA'19, 65 pages