A Functional-Neural Network for Post-Nonlinear Independent Component Analysis

Authors:
Oscar Fontenla-Romero;Bertha Guijarro-Berdiñas;Amparo Alonso-Betanzos
Affiliations:
-;-;-
Venue:
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Year:
2001

Citing 8
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Abstract

In this paper a hybrid approach, based on a functional network and a neural network, for post-nonlinear independent component analysis is presented. In order to obtain the independence among the outputs, it was used as cost function a measure based on Renyi's quadratic entropy and Caudy-Schwartz inequality Also, the Kernel method was used for nonparametric estimation of the probability density function. The experimental results corroborated the soundness of the approach and a comparative study with a neural networks and its superior performance.