Segmented principal component transform-partial least squares regression

• Type: Article in International Scientific Journal
• Date: 2007

Authors

Barros, AS (Author) Other; Pinto, R (Author) Other; Delgadillo, I (Author) Other; Rutledge, DN (Author) Other

Journal

Title: Chemometrics and Intelligent Laboratory Systems

Vol. 89

Pages: 59-68

ISSN: 0169-7439

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 3 Citations

Scopus - 3 Citations

Other information

Authenticus ID: P-004-69H

DOI: 10.1016/j.chemolab.2007.05.009

Abstract (EN): An approach for doing PLS on very wide datasets is proposed in this work. The method is based on the decomposition, by means of a SVD, of non-superimposed segments of the original data matrix. It is shown that this approach uses less computer resources compared to SIMPLS and PCT-PLS1. Furthermore, it is also shown that the results obtained by this approach are the same as those obtained by other regression methods (PLS and SIMPLS). The method implementation is simple and can be done in a distributed environment.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10