Computer Science – Neural and Evolutionary Computing
Scientific paper
2008-02-03
Chemometrics and Intelligent Laboratory Systems (2008)
Computer Science
Neural and Evolutionary Computing
A paraitre
Scientific paper
10.1016/j.chemolab.2007.09.004
Prediction problems from spectra are largely encountered in chemometry. In addition to accurate predictions, it is often needed to extract information about which wavelengths in the spectra contribute in an effective way to the quality of the prediction. This implies to select wavelengths (or wavelength intervals), a problem associated to variable selection. In this paper, it is shown how this problem may be tackled in the specific case of smooth (for example infrared) spectra. The functional character of the spectra (their smoothness) is taken into account through a functional variable projection procedure. Contrarily to standard approaches, the projection is performed on a basis that is driven by the spectra themselves, in order to best fit their characteristics. The methodology is illustrated by two examples of functional projection, using Independent Component Analysis and functional variable clustering, respectively. The performances on two standard infrared spectra benchmarks are illustrated.
François Damien
Krier Catherine
Rossi Fabrice
Verleysen Michel
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