Algorithms for multi‐group PLS. (22nd January 2014)

• Record Type: Journal Article
• Authors: Eslami, Aida; Qannari, El Mostafa; Kohler, Achim; Bougeard, Stéphanie
• Is Part Of: Journal of chemometrics. Volume 28:Number 3(2014:Mar.)
• Page Start: 192
• Page End: 201
• Publication Date: 2014-01-22
• Subjects: Chemistry -- Mathematics -- Periodicals; Chemistry -- Statistical methods -- Periodicals; 542.85
• DOI: 10.1002/cem.2593
• Languages: English
• ISSNs: 0886-9383

Abstract

<abstract abstract-type="main" id="cem2593-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="cem2593-para-0001">Several approaches of investigation of the relationships between two datasets where the individuals are structured into groups are discussed. These strategies fit within the framework of partial least squares (PLS) regression. Each strategy of analysis is introduced on the basis of a maximization criterion, which involves the covariances between components associated with the groups of individuals in each dataset. Thereafter, algorithms are proposed to solve these maximization problems. The strategies of analysis can be considered as extensions of multi‐group principal components analysis to the context of PLS regression. Copyright © 2014 John Wiley &amp; Sons, Ltd.</p> </abstract>