A New Neural Implementation of Exploratory Projection Pursuit

  • Colin Fyfe
  • Emilio Corchado
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2412)


We investigate an extension of the learning rules in a Principal Component Analysis network which has been derived to be optimal for a specific probability density function(pdf). We note that this probability density function is one of a family of pdfs and investigate the learning rules formed in order to be optimal for several members of this family. We show that, whereas previous authors [5] have viewed the single member of the family as an extension of PCA, it is more appropriate to view the whole family of learning rules as methods of performing Exploratory Projection Pursuit(EPP). We explore the performance of our method first in response to an artificial data type, then to a real data set.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Colin Fyfe
    • 1
  • Emilio Corchado
    • 1
  1. 1.Applied Computational Intelligence Research UnitThe University of PaisleyScotland

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