title: Faster Training in Nonlinear ICA using MISEP
creator: Almeida, Luis B.
subject: Statistical Models
subject: Machine Learning
subject: Neural Nets
description: MISEP has been proposed as a generalization of the INFOMAX method in two directions: (1) handling of nonlinear mixtures, and (2) learning the nonlinearities to be used at the outputs, making the method suitable to the separation of components with a wide range of statistical distributions. In all implementations up to now, MISEP had used multilayer perceptrons (MLPs) to perform the nonlinear ICA operation. Use of MLPs sometimes leads to a relatively slow training. This has been attributed, at least in part, to the non-local character of the MLP's units. This paper investigates the possibility of using a network of radial basis function (RBF) units for performing the nonlinear ICA operation. It shows that the local character of the RBF network's units allows a significant speedup in the training of the system. The paper gives a brief introduction to the basics of the MISEP method, and presents experimental results showing the speed advantage of using an RBF-based network to perform the ICA operation.
date: 2002
type: Conference Paper
type: NonPeerReviewed
format: application/pdf
identifier: http://cogprints.org/2691/1/AlmeidaICA2003.pdf
identifier:   Almeida, Luis B.  (2002) Faster Training in Nonlinear ICA using MISEP.  [Conference Paper]    (Unpublished)  
relation: http://cogprints.org/2691/