The analysis of high dimensional dataset is recurrently used in chemometrics where the data are presented in the form of digitized spectra (NIR). Statistical tool, as Discriminant Analysis, is frequently used in this field to classify object in predefined categories. But, by the fact that this kind of dataset presents the number of statistic units relatively small in comparison to the number of variables, the classical Discriminant Analysis can not be applied. In this paper, the authors, present a strategy to choose an optimal subset of predictors to perform Discriminant Analysis on NIR data in partial least squares framework.

Variable Selection in PLS Discriminant Analysis via the Disco

Simonetti B;LUCADAMO, ANTONIO;
2012

Abstract

The analysis of high dimensional dataset is recurrently used in chemometrics where the data are presented in the form of digitized spectra (NIR). Statistical tool, as Discriminant Analysis, is frequently used in this field to classify object in predefined categories. But, by the fact that this kind of dataset presents the number of statistic units relatively small in comparison to the number of variables, the classical Discriminant Analysis can not be applied. In this paper, the authors, present a strategy to choose an optimal subset of predictors to perform Discriminant Analysis on NIR data in partial least squares framework.
Spectroscopic data; Linear Discriminant Analysis; Discriminant Partial least squares; Guttman-Raveh DisCo index
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12070/5797
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