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A twist to partial least squares regression
Author(s) -
Indahl Ulf
Publication year - 2005
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.904
Subject(s) - benchmark (surveying) , mathematics , calibration , partial least squares regression , statistic , set (abstract data type) , limit (mathematics) , selection (genetic algorithm) , flexibility (engineering) , model selection , regression , cross validation , algorithm , statistics , mathematical optimization , computer science , artificial intelligence , mathematical analysis , geodesy , programming language , geography
A modification of the PLS1 algorithm is presented. Stepwise optimization over a set of candidate loading weights obtained by taking powers of the y – X correlations and X standard deviations generalizes the classical PLS1 based on y – X covariances and hence adds flexibility to the modelling. When good linear predictions can be obtained, the suggested approach often finds models with fewer and more interpretable components. Good performance is demonstrated when compared with the classical PLS1 on calibration benchmark data sets. An important part of the comparisons is managed by a novel model selection strategy. The selection is based on choosing the simplest model among those with a cross‐validation error smaller than the pre‐specified significance limit of a χ 2 ‐statistic. Copyright © 2005 John Wiley & Sons, Ltd.