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Publisert 2009

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Publikasjonsdetaljer

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

Internasjonale standardnummer :
Trykt : 0886-9383
Elektronisk : 1099-128X

Publikasjonstype : Vitenskapelig artikkel

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

Sak : 9-10

Har du spørsmål om noe vedrørende publikasjonen, kan du kontakte Nofimas bibliotekleder.

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.

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