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.
Keywords: Spectroscopic data, Linear Discriminant Analysis, Discriminant Partial least squares, Guttman-Raveh DisCo index
Current Analytical Chemistry
Title:Variable Selection in PLS Discriminant Analysis via the Disco
Volume: 8 Issue: 2
Author(s): Biagio Simonetti, Antonio Lucadamo and Maria R. G. Rodriguez
Affiliation:
Keywords: Spectroscopic data, Linear Discriminant Analysis, Discriminant Partial least squares, Guttman-Raveh DisCo index
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.
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Cite this article as:
Simonetti Biagio, Lucadamo Antonio and R. G. Rodriguez Maria, Variable Selection in PLS Discriminant Analysis via the Disco, Current Analytical Chemistry 2012; 8 (2) . https://dx.doi.org/10.2174/157341112800392625
DOI https://dx.doi.org/10.2174/157341112800392625 |
Print ISSN 1573-4110 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-6727 |
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