# Automatic Regularization for Linear MMSE Filters

arXiv: https://arxiv.org/abs/2312.06560 · HTML: https://mlsanity.com/paper/2312.06560/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 21 references that are in the map.
- First posted on arXiv 11 Dec 2023.

## Built on by (most cited first, 1 of 1)

- [Low-rank MMSE filters, Kronecker-product representation, and regularization: a new perspective](https://mlsanity.com/paper/2512.14932/) (2025)

## Abstract (arXiv)

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.