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.

翻译：本文考虑最小均方误差线性滤波器中的正则化问题。利用与统计机器学习方法的关系，从观测信号中以简单自动的方式确定正则化参数。通过系统辨识示例验证了所提方法，其中自动正则化可产生接近最优的结果。