3 research outputs found
A stochastic behavior analysis of stochastic restricted-gradient descent algorithm in reproducing kernel Hilbert spaces
This paper presents a stochastic behavior analysis of a kernel-based
stochastic restricted-gradient descent method. The restricted gradient gives a
steepest ascent direction within the so-called dictionary subspace. The
analysis provides the transient and steady state performance in the mean
squared error criterion. It also includes stability conditions in the mean and
mean-square sense. The present study is based on the analysis of the kernel
normalized least mean square (KNLMS) algorithm initially proposed by Chen et
al. Simulation results validate the analysis