Post processing methods (PLS–CCA): simple alternatives to preprocessing methods (OSC–PLS)

Orthogonal signal correction (OSC) methods have been proposed as a way of preprocessing data prior to performing PLS regression. The purpose is generally not to improve the prediction but to remove variation in X that is uncorrelated with Y in order to simplify both the structure and interpretation of the resulting PLS regression model. This paper introduces an alternative approach based on post-processing a standard PLS model with canonical correlation analysis (CCA). It is shown that this is only one of a class of post-processing methods which have certain advantages over most preprocessing approaches using OSC.