Low-rank Approximation of Linear Maps
Machine Learning
2023-01-09 v2 Machine Learning
Abstract
This work provides closed-form solutions and minimum achievable errors for a large class of low-rank approximation problems in Hilbert spaces. The proposed theorem generalizes to the case of bounded linear operators the previous results obtained in the finite dimensional case for the Frobenius norm. The theorem provides the basis for the design of tractable algorithms for kernel or continuous DMD.
Cite
@article{arxiv.1812.09042,
title = {Low-rank Approximation of Linear Maps},
author = {Patrick Heas and Cedric Herzet},
journal= {arXiv preprint arXiv:1812.09042},
year = {2023}
}