Combined nonlinear filtering architectures involving sparse functional link adaptive filters
Sparsity phenomena in learning processes have been extensively studied, since their detection allows to derive suited regularized optimization algorithms capable of improving the overall learning performance. In this paper, we investigate the sparsity behavior that may occur in nonlinear adaptive filtering problems and how to leverage it and develop enhanced algorithms. In particular, we focus on a particular class of linear-in-the-parameters nonlinear adaptive filters, whose nonlinear transformation is based on a functional link expansion.