A data-driven functional projection approach for the selection of feature ranges in spectra with ICA or cluster analysis - Archive ouverte HAL Access content directly
Journal Articles Chemometrics and Intelligent Laboratory Systems Year : 2008

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

Abstract

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.
Fichier principal
Vignette du fichier
CILS_final.pdf (297.27 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

inria-00232874 , version 1 (02-02-2008)

Identifiers

Cite

Catherine Krier, Fabrice Rossi, Damien François, Michel Verleysen. A data-driven functional projection approach for the selection of feature ranges in spectra with ICA or cluster analysis. Chemometrics and Intelligent Laboratory Systems, 2008, ⟨10.1016/j.chemolab.2007.09.004⟩. ⟨inria-00232874⟩

Collections

INRIA INRIA2
132 View
161 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More