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International Work-Conference on Artificial Neural Networks

IWANN 2001: Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence pp 301–307Cite as

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

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2084))

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 Cauchy-Sc hw artz 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 network showed its superior performance.

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Author information

Authors and Affiliations

  1. Laboratory for Research and Development in Artificial Intelligence, Department of Computer Science, University of A Coruña, Campus de Elviña s/n, 15071, A Coruña, Spain

    Oscar Fontenla Romero, Bertha Guijarro Berdiñas & Amparo Alonso Betanzos

Authors
  1. Oscar Fontenla Romero
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  2. Bertha Guijarro Berdiñas
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  3. Amparo Alonso Betanzos
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Editor information

Editors and Affiliations

  1. Departamento de Inteligencia Artificial Sanda del Rey, Universidad Nacional de Educación a Distancia, s/n., Madrid, 28040, Spain

    José Mira

  2. Departamento de Arquitectura y Tecnología de Computadores, Universidad de Granada, Campus Fuentenueva, 18071, Granada, Spain

    Alberto Prieto

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© 2001 Springer-Verlag Berlin Heidelberg

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Romero, O.F., Berdiñas, B.G., Betanzos, A.A. (2001). A Functional-Neural Network for Post-Nonlinear Independent Component Analysis. In: Mira, J., Prieto, A. (eds) Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence. IWANN 2001. Lecture Notes in Computer Science, vol 2084. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45720-8_34

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  • DOI: https://doi.org/10.1007/3-540-45720-8_34

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42235-8

  • Online ISBN: 978-3-540-45720-6

  • eBook Packages: Springer Book Archive

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