Recursive Hyperparameter-Free Criterion Learning

Abstract

In the context of adaptive signal processing for non-Gaussian noise scenarios, the paradigm of information theoretic learning (ITL) has emerged useful due to their incorporation of higher order error-statistics, their improved convergence, and for their motivation from the standpoint of statistical mechanics. However, these ITL criteria are well-known to depend on scenario-dependent hyperparameter choices, whose optimal values, in-turn, depend on scenario dependent noise-statistics. This brief proposes hyperparameter free criterion learning using random Fourier features (RFF), which alleviates hyperparameter-dependence, and allows for scenario-independent generalization for underlying noise-distributions. For the proposed approach, detailed convergence analysis is presented and validated via relevant case-studies.