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Computational Statistics

, Volume 22, Issue 2, pp 223–235 | Cite as

PLS classification of functional data

  • Cristian PredaEmail author
  • Gilbert Saporta
  • Caroline Lévéder
Original Paper

Abstract

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.

Keywords

PLS regression Functional data Linear discriminant analysis 

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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Cristian Preda
    • 1
    Email author
  • Gilbert Saporta
    • 2
  • Caroline Lévéder
    • 3
  1. 1.CERIM - Département de Statistique, Faculté de MédecineUniversité de Lille 2LilleFrance
  2. 2.Chaire de Statistique Appliquée, CEDRIC, CNAMParis Cedex 03France
  3. 3.Danone VitapolePalaiseau CedexFrance

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