Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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A Robust Principal Component Neural Network

  • Changha Hwang (Associate Professor, Dept, of Statistical Information, Catholic University of Daegu) ;
  • Park, Hyejung (Computer Education Center, Catholic University of Daegu) ;
  • A, Eunyoung-N (Dept. of Statistical Information, Catholic University of Daegu)
  • Published : 2001.12.01
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Abstract

Principal component analysis(PCA) is a multivariate technique falling under the general title of factor analysis. The purpose of PCA is to Identify the dependence structure behind a multivariate stochastic observation In order to obtain a compact description of it. In engineering field PCA is utilized mainly (or data compression and restoration. In this paper we propose a new robust Hebbian algorithm for robust PCA. This algorithm is based on a hyperbolic tangent function due to Hampel ef al.(1989) which is known to be robust in Statistics. We do two experiments to investigate the performance of the new robust Hebbian learning algorithm for robust PCA.

Keywords

References

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