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Cross fitted partial least squares (CF-PLS): an alternative algorithm for a more reliable PLS

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Abstract

This paper presents a modified version of the NIPALS algorithm for PLS regression with one single response variable. This version, denoted a CF-PLS, provides significant advantages over the standard PLS. First of all, it strongly reduces the over-fit of the regression. Secondly, R2 for the null hypothesis follows a Beta distribution only function of the number of observations, which allows the use of a probabilistic framework to test the validity of a component. Thirdly, the models generated with CF-PLS have comparable if not better prediction ability than the models fitted with NIPALS. Finally, the scores and loadings of the CF-PLS are directly related to the R2, which makes the model and its interpretation more reliable. Copyright © 2011 John Wiley & Sons, Ltd.

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