Principal components transform-partial least squares: a novel method to accelerate cross-validation in PLS regression

• Type: Article in International Scientific Journal
• Date: 2004

Authors

Barros, AS (Author) Other; Rutledge, DN (Author) Other

Journal

Title: Chemometrics and Intelligent Laboratory Systems

Vol. 73

Pages: 245-255

ISSN: 0169-7439

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 20 Citations

Scopus - 25 Citations

Other information

Authenticus ID: P-000-825

DOI: 10.1016/j.chemolab.2004.03.007

Abstract (EN): This work proposes a new approach for building PLS regression models, Principal Components Transfonn-PLS (PCT-PLS), which is based on a full eigen decomposition (NIPALS) of the X matrix before proceeding to the PLS regression. This method dramatically accelerates the cross-validation of the calibration models and is at the same time parsimonious in computer memory requirements. This is most noticeable for the huge data sets that are common nowadays. This new approach preserves all the PLS modeling properties, such as robustness and regression vector interpretability, thus facilitating the application of this new procedure to building calibration models. The proposed technique will allow the application of PLS modeling to much larger data sets than was previously feasible.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11