Affordable Access

deepdyve-link
Publisher Website

Kernel recursive least-squares tracker for time-varying regression.

Authors
  • Van Vaerenbergh, Steven
  • Lázaro-Gredilla, Miguel
  • Santamaria, Ignacio
Type
Published Article
Journal
IEEE transactions on neural networks and learning systems
Publication Date
Aug 01, 2012
Volume
23
Issue
8
Pages
1313–1326
Identifiers
DOI: 10.1109/TNNLS.2012.2200500
PMID: 24807527
Source
Medline
License
Unknown

Abstract

In this paper, we introduce a kernel recursive least-squares (KRLS) algorithm that is able to track nonlinear, time-varying relationships in data. To this purpose, we first derive the standard KRLS equations from a Bayesian perspective (including a sensible approach to pruning) and then take advantage of this framework to incorporate forgetting in a consistent way, thus enabling the algorithm to perform tracking in nonstationary scenarios. The resulting method is the first kernel adaptive filtering algorithm that includes a forgetting factor in a principled and numerically stable manner. In addition to its tracking ability, it has a number of appealing properties. It is online, requires a fixed amount of memory and computation per time step, incorporates regularization in a natural manner and provides confidence intervals along with each prediction. We include experimental results that support the theory as well as illustrate the efficiency of the proposed algorithm.

Report this publication

Statistics

Seen <100 times