Automatic Regularization for Linear MMSE Filters
Daniel Gomes de Pinho Zanco Leszek Szczecinski Jacob Benesty
In this work, we consider the problem of regularization in minimum
mean-squared error (MMSE) linear filters. Exploiting the relationship with
statistical machine learning methods, the regularization parameter is found
from the observed signals in a simple and automatic manner. The proposed
approach is illustrated through system identification examples, where the
automatic regularization yields near-optimal results.