A New Effective Algorithm for Stepwise Principle Components Selection in Discriminant Analysis
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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.
KeywordsError Probability Principle Component Average Decrease Small Dispersion Component Number
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