Recursive Hyperparameter-Free Criterion Learning

doi:10.1109/TCSII.2022.3187922

Recursive Hyperparameter-Free Criterion Learning

2022-11-01

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PRE

AI

DOI：10.1109/TCSII.2022.3187922

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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.

Keyword:

Convergence

Additive noise

Performance analysis

Inspection

Gaussian noise

Floors

Finite impulse response filters

RFF

criterion learning

hyperparameter-free learning

Recursive Hyperparameter-Free Criterion Learning

摘要

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