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Partial Least Square Regression PLS-Regression
 

Summary: Partial Least Square Regression
PLS-Regression
Hervé Abdi1
1 Overview
PLS regression is a recent technique that generalizes and combines
features from principal component analysis and multiple regres-
sion. Its goal is to predict or analyze a set of dependent variables
from a set of independent variables or predictors. This predic-
tion is achieved by extracting from the predictors a set of orthog-
onal factors called latent variables which have the best predictive
power.
PLS regression is particularly useful when we need to predict
a set of dependent variables from a (very) large set of indepen-
dent variables (i.e., predictors). It originated in the social sciences
(specifically economy, Herman Wold 1966) but became popular
first in chemometrics (i.e., computational chemistry) due in part
to Herman's son Svante, (Wold, 2001) and in sensory evaluation
(Martens & Naes, 1989). But PLS regression is also becoming a tool
of choice in the social sciences as a multivariate technique for non-
experimental and experimental data alike (e.g., neuroimaging, see

  

Source: Abdi, Hervé - School of Behavioral and Brain Sciences, University of Texas at Dallas

 

Collections: Biology and Medicine; Computer Technologies and Information Sciences