Canonical partial least squares-a unified PLS approach to classification and regression problems

Published 2009

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Publication details

Journal : Journal of Chemometrics , vol. 23 , p. 495–504–10 , 2009

International Standard Numbers :
Printed : 0886-9383
Electronic : 1099-128X

Publication type : Academic article

Contributors : Indahl, Ulf; Liland, Kristian Hovde; Næs, Tormod

Issue : 9-10

Links :
OMTALE : http://dx.doi.org/doi:10.1002/...
DOI : doi.org/10.1002/cem.1243

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Summary

We propose a new data compression method for estimating optimal latent variables in multi-variate classification and regression problems where more than one response variable is available. The latent variables are found according to a common innovative principle combining PLS methodology and canonical correlation analysis (CCA). The suggested method is able to extract predictive information for the latent variables more effectively than ordinary PLS approaches. Only simple modifications of existing PLS and PPLS algorithms are required to adopt the proposed method. Copyright (C) 2009 John Wiley & Sons, Ltd.