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Performance evaluation and dynamic node generation criteria for ‘principal component analysis’ neural networks

Author

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  • Tzafestas, E.S.
  • Nikolaidou, A.
  • Tzafestas, S.G.

Abstract

This paper is concerned with the solution of the principal component analysis (PCA) problem with the aid of neural networks (NNs). After an overview of the basic NN-based PCA concepts and a listing of the available algorithms, two criteria for evaluating PCA NN algorithms are proposed. Then, a new criterion for the generation of improved PCA NN structures with reduced size is presented. Using this criterion, one can start with a small network and dynamically add new nodes at the hidden layer(s) during training, one at a time, until the desired performance is achieved. A simulation example is provided that shows the applicability and effectiveness of the methodology.

Suggested Citation

  • Tzafestas, E.S. & Nikolaidou, A. & Tzafestas, S.G., 2000. "Performance evaluation and dynamic node generation criteria for ‘principal component analysis’ neural networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 51(3), pages 145-156.
    • Handle: RePEc:eee:matcom:v:51:y:2000:i:3:p:145-156
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    References listed on IDEAS

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    1. Tzafestas, S.G. & Dalianis, P.J. & Anthopoulos, G., 1996. "On the overtraining phenomenon of backpropagation neural networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 40(5), pages 507-521.
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