Low-rank MMSE filters, Kronecker-product representation, and regularization: a new perspective
Machine Learning
2025-12-18 v1
Abstract
In this work, we propose a method to efficiently find the regularization parameter for low-rank MMSE filters based on a Kronecker-product representation. We show that the regularization parameter is surprisingly linked to the problem of rank selection and, thus, properly choosing it, is crucial for low-rank settings. The proposed method is validated through simulations, showing significant gains over commonly used methods.
Keywords
Cite
@article{arxiv.2512.14932,
title = {Low-rank MMSE filters, Kronecker-product representation, and regularization: a new perspective},
author = {Daniel Gomes de Pinho Zanco and Leszek Szczecinski and Jacob Benesty and Eduardo Vinicius Kuhn},
journal= {arXiv preprint arXiv:2512.14932},
year = {2025}
}