Regularized Kernel Recursive Least Square Algoirthm

TL;DR

This paper discusses a regularized kernel recursive least squares algorithm that improves adaptive signal processing by combining kernel methods with sparsification techniques to reduce computational complexity while maintaining high accuracy.

Contribution

It introduces a regularized kernel recursive least squares algorithm that enhances efficiency through sparsification, balancing accuracy and computational cost.

Findings

Achieves high accuracy in stationary scenarios

Reduces computational complexity via sparsification

Maintains fast convergence rate

Abstract

In most adaptive signal processing applications, system linearity is assumed and adaptive linear filters are thus used. The traditional class of supervised adaptive filters rely on error-correction learning for their adaptive capability. The kernel method is a powerful nonparametric modeling tool for pattern analysis and statistical signal processing. Through a nonlinear mapping, kernel methods transform the data into a set of points in a Reproducing Kernel Hilbert Space. KRLS achieves high accuracy and has fast convergence rate in stationary scenario. However the good performance is obtained at a cost of high computation complexity. Sparsification in kernel methods is know to related to less computational complexity and memory consumption.