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Neural PCA and Maximum Likelihood Hebbian Learning on the GPU

  • Pavel Krömer
  • Emilio Corchado
  • Václav Snášel
  • Jan Platoš
  • Laura García-Hernández
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7553)

Abstract

This study introduces a novel fine-grained parallel implementation of a neural principal component analysis (neural PCA) variant and the maximum Likelihood Hebbian Learning (MLHL) network designed for modern many-core graphics processing units (GPUs). The parallel implementation as well as the computational experiments conducted in order to evaluate the speedup achieved by the GPU are presented and discussed. The evaluation was done on a well-known artificial data set, the 2D bars data set.

Keywords

neural PCA Maximum Likelihood Hebbian Learning Exploratory Projection Pursuit GPU CUDA performance 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Pavel Krömer
    • 1
    • 2
  • Emilio Corchado
    • 2
    • 3
  • Václav Snášel
    • 1
    • 2
  • Jan Platoš
    • 1
    • 2
  • Laura García-Hernández
    • 4
  1. 1.Department of Computer ScienceVŠB-Technical University of OstravaOstrava-PorubaCzech Republic
  2. 2.IT4InnovationsOstrava-PorubaCzech Republic
  3. 3.Departamento de Informática y AutomáticaUniversidad de SalamancaSpain
  4. 4.Area of Project EngineeringUniversity of CordobaSpain

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