Automatic Regularization for Linear MMSE Filters
Information Theory
2024-07-01 v2 Machine Learning
Signal Processing
math.IT
Abstract
In this work, we consider the problem of regularization in the design of minimum mean square error (MMSE) linear filters. Using the relationship with statistical machine learning methods, using a Bayesian approach, the regularization parameter is found from the observed signals in a simple and automatic manner. The proposed approach is illustrated in system identification and beamforming examples, where the automatic regularization is shown to yield near-optimal results.
Cite
@article{arxiv.2312.06560,
title = {Automatic Regularization for Linear MMSE Filters},
author = {Daniel Gomes de Pinho Zanco and Leszek Szczecinski and Jacob Benesty},
journal= {arXiv preprint arXiv:2312.06560},
year = {2024}
}