- Research Article
- Open Access
Kernel Affine Projection Algorithms
EURASIP Journal on Advances in Signal Processing volume 2008, Article number: 784292 (2008)
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.
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Liu, W., Príncipe, J.C. Kernel Affine Projection Algorithms. EURASIP J. Adv. Signal Process. 2008, 784292 (2008) doi:10.1155/2008/784292
- Neural Network
- Information Technology
- Quantum Information
- Unify Model
- Computation Complexity