Overview of the Partial Least Squares Platform
In contrast to ordinary least squares, PLS can be used when the predictors outnumber the observations. PLS is used widely in modeling high-dimensional data in areas such as spectroscopy, chemometrics, genomics, psychology, education, economics, political science, and environmental science.
The PLS approach to model fitting is particularly useful when there are more explanatory variables than observations or when the explanatory variables are highly correlated. You can use PLS to fit a single model to several responses simultaneously. (Wold, 1995; Wold et al, 2001, Eriksson et al, 2006).
Two model fitting algorithms are available: nonlinear iterative partial least squares (NIPALS) and a “statistically ...
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