Convergence analysis of Local Feature Extraction Algorithms
Resource
Neural Networks, n.5 p.229-240
Journal
Neural Networks
Journal Issue
5
Pages
229-240
Date Issued
1992-01
Author(s)
K. Hornik
Abstract
We investigate the asymptotic behavior of a general class of on-line Principal Component Analysis (PCA) learning algorithms, focusing our attention on the analysis of two algorithms which have recently been proposed and are based on strictly local learning rules. We rigorously establish that the behavior of the algorithms is intimately related to an ordinary differential equation (ODE) which is obtained by suitably averaging over the training patterns, and study the equilibria of these ODEs and their local stability properties. Our results imply, in particular, that local PCA algorithms should always incorporate hierarchical rather than more competitive, symmetric decorrelation, for reasons of superior performance of the algorithms. © 1992 Pergamon Press Ltd.
Type
journal article