Minimizing Fisher information of the error in supervised adaptive filter training
In this paper, we propose minimizing the Fisher information of the error in supervised training of linear and nonlinear adaptive filters. Fisher information considers the local structure of the error probability distribution and therefore it is a criterion that deserves to be investigated as an alternative to more common statistics such as minimum mean-square-error or minimum-error-entropy. A gradient-based training algorithm, based on a nonparametric estimator of Fisher information is presented and the performances of the three mentioned optimization criteria are compared using Monte Carlo simulations.