The combination of the famed kernel trick and affine projection algorithms (APAs) yields
powerful nonlinear extensions, named collectively here, KAPA. This paper is a follow-up …

In this paper, a sparsity promoting adaptive algorithm for distributed learning in diffusion
networks is developed. The algorithm follows the set-theoretic estimation rationale. At each …

This paper presents a family of robust set-membership normalized subband adaptive
filtering (RSM-NSAF) algorithms for acoustic echo cancellation (AEC). By using a new robust …

The proportionate normalized least mean square (PNLMS) algorithm and its variants are by
far the most popular adaptive filters that are used to identify sparse systems. The …

In order to increase the convergence rate of the normalized least mean square (NLMS)
algorithm for highly correlated signals, a family of adaptive decorrelation NLMS variants is …

We propose a novel adaptive learning algorithm based on iterative orthogonal projections in
the Cartesian product of multiple reproducing kernel Hilbert spaces (RKHSs). The objective …

In this paper, a new convergence analysis is presented for a well-known sparse adaptive
filter family, namely, the proportionate-type normalized least mean square (PtNLMS) …

First, we study a basic kernel adaptive filtering algorithm based on stochastic restricted-
gradient, which is a natural extension of the naive online regularized risk minimization …

We propose a novel online learning paradigm for nonlinear-function estimation tasks based
on the iterative projections in the L 2 space with probability measure reflecting the stochastic …

Wireless communication channels are highly prone to interference in addition to the
presence of additive white Gaussian noise (AWGN). Stochastic gradient (SG)-based non …