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Faster Training in Nonlinear ICA using MISEP

Almeida, Luis B. (2002) Faster Training in Nonlinear ICA using MISEP. [Conference Paper] (Unpublished)

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Abstract

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.

Item Type:Conference Paper
Keywords:Independent components analysis, nonlinear, blind source separation, ICA, BSS
Subjects:Computer Science > Statistical Models
Computer Science > Machine Learning
Computer Science > Neural Nets
ID Code:2691
Deposited By: Almeida, Prof. Luis B.
Deposited On:07 Jan 2003
Last Modified:11 Mar 2011 08:55

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References in Article

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