PLS Discriminant Analysis - A comparative study
PLS regression is a regression technique usually designed to predict the values taken by a group of Y variables (target variables, dependent variables) from a set of variables X (descriptors, independent variables). Initially defined for the prediction of continuous target variable, the PLS regression can be adapted to the prediction of one discrete variable - i.e. adapted to the supervised learning framework - in different ways . The approah is called "PLS Discriminant Analysis" in this context. It incorporates the valuable qualities that we know usually into this new framework: the ability to process a representation space with very high dimensionality, a large number of noisy and / or redundant descriptors. This tutorial is the continuation of a precedent paper dedicated to the presentation of some variants of the PLS-DA . We describe the behavior of one of them (PLS-LDA - PLS Linear Discriminant Analysis) on a learning set where the number of descriptors is moderately hig...