A data-driven functional projection approach for the selection of feature ranges in spectra with ICA or cluster analysis

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dc.creatorKrier, Catherine
dc.creatorRossi, Fabrice
dc.creatorFrançois, Damien
dc.creatorVerleysen, Michel
dc.date2008-02-03
dc.date.accessioned2026-07-07T09:18:28Z
dc.date.available2026-07-07T09:18:28Z
dc.descriptionPrediction 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.
dc.descriptionA paraitre
dc.identifierhttps://arxiv.org/abs/0802.0287
dc.identifierhttp://arxiv.org/abs/0802.0287
dc.identifierChemometrics and Intelligent Laboratory Systems (2008)
dc.identifierdoi:10.1016/j.chemolab.2007.09.004
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/154038
dc.subjectNeural and Evolutionary Computing
dc.titleA data-driven functional projection approach for the selection of feature ranges in spectra with ICA or cluster analysis
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