Partial Least Squares (PLS) Regression

Pls regression is a recent technique that generalizes and combines features from principal component analysis and multiple regression. It is particularly useful when we need to predict a set of dependent variables from a (very) large set of independent 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, (see, e.g., Geladi & Kowalski, 1986) 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 Mcintosh, Bookstein, Haxby, & Grady, 1996). It was first presented as an algorithm akin to the power method (used for computing eigenvectors) but was rapidly interpreted in a statistical framework. (Frank, & Friedman, 1993; Helland, 1990; Höskuldsson, 1988; Tenenhaus, 1998).