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Canonical partial least squares—a unified PLS approach to classification and regression problems
Author(s) -
Indahl Ulf G.,
Liland Kristian Hovde,
Næs Tormod
Publication year - 2009
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1243
Subject(s) - partial least squares regression , latent variable , canonical correlation , simple (philosophy) , regression , mathematics , regression analysis , simple linear regression , statistics , artificial intelligence , latent variable model , computer science , pattern recognition (psychology) , philosophy , epistemology
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 © 2009 John Wiley & Sons, Ltd.