A stochastic behavior analysis of stochastic restricted-gradient descent algorithm in reproducing kernel Hilbert spaces
This is an incremental analysis for researchers in kernel methods and adaptive filtering, focusing on theoretical performance without new empirical results.
The paper analyzes the stochastic behavior of a kernel-based stochastic restricted-gradient descent method, providing transient and steady-state performance in mean squared error and stability conditions.
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.