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

✍ Scribed by Ulf G. Indahl; Kristian Hovde Liland; Tormod Næs

Publisher
John Wiley and Sons
Year
2009
Tongue
English
Weight
303 KB
Volume
23
Category
Article
ISSN
0886-9383

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✦ Synopsis

Abstract

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.

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