Canonical partial least squares-a unified PLS approach to classification and regression problems
Publikasjonsdetaljer
Tidsskrift : Journal of Chemometrics , vol. 23 , p. 495–504–10 , 2009
Internasjonale standardnummer
:
Trykt
:
0886-9383
Elektronisk
:
1099-128X
Publikasjonstype : Vitenskapelig artikkel
Sak : 9-10
Lenker
:
OMTALE
:
http://dx.doi.org/doi:10.1002/...
DOI
:
doi.org/10.1002/cem.1243
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Kjetil Aune
Bibliotekleder
kjetil.aune@nofima.no
Sammendrag
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.