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

Catherine Krier (1) , Fabrice Rossi (2) , Damien François (3) , Michel Verleysen (1)

1 DICE - MLG - Dispositifs Intégrés et Circuits Electroniques Machine Learning Group
2 AxIS - Usage-centered design, analysis and improvement of information systems
3 CSAM - Centre for Systems Engineering and Applied Mechanics

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.

Mots clés: feature extraction; functional projection; Independent Component Analysis; clustering

Domaines: Réseau de neurones [cs.NE]; Chemo-informatique

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⟩