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A New Effective Algorithm for Stepwise Principle Components Selection in Discriminant Analysis

  • Ekaterina Serikova
  • Eugene Zhuk
Conference paper
  • 1.6k Downloads
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

The problem of reducing the dimensionality of multivariate Gaussian observations is considered. The efficiency of discriminant analysis procedure based on well-known method of principle components selection is analytically investigated. The average decrease of interclass distances square is presented as a new criterion of feature selection directly connected with the classification error probability. New stepwise discriminant analysis procedure in the space of principal components based on this criterion is proposed and its efficiency is experimentally and analytically investigated.

Keywords

Error Probability Principle Component Average Decrease Small Dispersion Component Number 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. AIVAZYAN S., BUCHSTABER V., YENYUKOV I., MESHALKIN L. (1989): Applied statistics: Classification and Dimensionality Reduction. Finansy i Statistika, Moskow.Google Scholar
  2. ANDERSON Y. (1963): An Introduction to Multivariate Statistical Analysis. Viley, New York.Google Scholar
  3. FUKUNAGA K. (1990): Introduction to statistical pattern recognition. Academic Press, New York.Google Scholar
  4. SERIKOVA E. (2004): Admissible sample size for stepwise discriminant procedure based on interclass distance behavior. Computer Data Analysis and Modeling: robustness and computer intensive methods. September, Minsk, 189–192.Google Scholar

Copyright information

© Springer Berlin · Heidelberg 2006

Authors and Affiliations

  • Ekaterina Serikova
    • 1
  • Eugene Zhuk
    • 1
  1. 1.Belarus State UniversityMinskBelarus

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