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<p>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).</p>
optimal transformation; principal component analysis; multivariate data sets; discriminant analysis; discriminant vectors; computerised pattern recognition; vectors
S. Leclerq, L. Duchene, "An Optimal Transformation for Discriminant and Principal Component Analysis", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 10, no. , pp. 978-983, November 1988, doi:10.1109/34.9121
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