Authors: Manuel Urbano Cuadrado Gonzalo Cerruela García Irene Luque Ruiz Miguel Ángel GómezNieto
Publish Date: 2006/06/16
Volume: 40, Issue: 1, Pages: 15-27
Abstract
A method for the treatment of longdimensional chemical data arrays is presented in this work with the aim of maximising classification models The method is based on the construction of fingerprints and the subsequent generation of a similarity matrix The similarity calculation has been modified through a scaling process to take into account different significance shown by the variables The method was applied to spectral measurements of wines and several aspects were studied namely threshold considered in the construction of fingerprints and patterns weighting factor used for scaling normalisation method etc The application of both Principal Components Analysis and SoftIndependent Modelling of Class Analogies to the similarity matrices gave better classifications of the information than those obtained using original data
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