An Optimal Transformation for Discriminant and Principal Component Analysis

Authors:
J. Duchene;S. Leclercq
Affiliations:
Compie´gne Univ., Compie´gne, France;Compie´gne Univ., Compie´gne, France
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1988

Citing 2
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Abstract

A general method is proposed to describe multivariate data sets using discriminant analysis and principal-component analysis. First, the problem of finding K discriminant vectors in an L-class data set is solved and compared to the solution proposed in the literature for two-class problems and the classical solution for L-class data sets. It is shown that the method proposed is better than the classical method for L classes and is a generalization of the optimal set of discriminant vectors proposed for two-class problems. Then the method is combined with a generalized principal-component analysis to permit the user to define the properties of each successive computed vector. All the methods were tested using measurements made on various kinds of flowers (IRIS data).