Kernel Affine Projection Algorithms

Open Access
Research Article
Part of the following topical collections:
  1. Emerging Machine Learning Techniques in Signal Processing

Abstract

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 study of the recently introduced kernel least-mean-square algorithm (KLMS). KAPA inherits the simplicity and online nature of KLMS while reducing its gradient noise, boosting performance. More interestingly, it provides a unifying model for several neural network techniques, including kernel least-mean-square algorithms, kernel adaline, sliding-window kernel recursive-least squares (KRLS), and regularization networks. Therefore, many insights can be gained into the basic relations among them and the tradeoff between computation complexity and performance. Several simulations illustrate its wide applicability.

Keywords

Neural Network Information Technology Quantum Information Unify Model Computation Complexity 

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Copyright information

© W. Liu and J. C. Príncipe 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of FloridaGainesvilleUSA

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