Combining bilinear modelling and ridge regression

Published 2002

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Publication details

Journal : Journal of Chemometrics , vol. 16 , p. 313–318 , 2002

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

Publication type : Academic article

Contributors : Høy, Martin; Westad, Frank Ove; Martens, Harald

Links :
DOI : doi.org/10.1002/cem.727

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Kjetil Aune
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Summary

A method is presented for making principal component regression (PCR), partial least squares regression (PLSR) and other regressions based on bilinear modelling (BLM) less sensitive to overfit. The idea is to use generalized ridge regression to calculate the Y-loadings in order to prevent small, uncertain values of the score vectors from causing inflation of variance in the regression coefficients. Thus we combine the stabilizing power of ridge regression with the modelling power and interpretability of bilinear models. The method is intended to provide better predictive ability and improved stability for regression models. Copyright (C) 2002 John Wiley Sons, Ltd.