Cross fitted partial least squares (CF-PLS): an alternative algorithm for a more reliable PLS
✍ Scribed by Olivier Cloarec
- Publisher
- John Wiley and Sons
- Year
- 2011
- Tongue
- English
- Weight
- 559 KB
- Volume
- 25
- Category
- Article
- ISSN
- 0886-9383
- DOI
- 10.1002/cem.1380
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✦ Synopsis
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, R^2^ 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 R^2^, which makes the model and its interpretation more reliable. Copyright © 2011 John Wiley & Sons, Ltd.