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[PSL07a] PLS classification of functional data

Revue Internationale avec comité de lecture : Journal Computational Statistics, vol. 22(2), pp. 223-235, 2007
motcle:
Résumé: Partial least squares (PLS) approach is proposed for linear discriminant analysis (LDA) when predictors are data of functional type (curves). Based on the equivalence between LDA and the multiple linear regression (binary response) and LDA and the canonical correlation analysis (more than two groups), the PLS regression on functional data is used to estimate the discriminant coefficient functions. A simulation study as well as an application to kneading data compare the PLS model results with those given by other methods.

Equipe: msdma

BibTeX

@article {
PSL07a,
title="{PLS classification of functional data}",
author="C. Preda and G. Saporta and C. Leveder",
journal="Computational Statistics",
year=2007,
volume=22,
number=2,
pages="223-235",
}