Multiclass Partial Least Squares Discriminant Analysis: Taking New Ways—A Perspective

ABSTRACT An overview of various prospective PLS‐DA strategies is presented. PLS‐DA is considered not only as a discrimination method per se but also as a method for extracting low‐dimensional features suitable for discriminating high‐dimensional data, which makes it similar to PCA. Unlike PCA, PLS‐DA extracts two sets of features: the matrix of predicted dummy responses ( Y ) and the matrix of PLS scores ( T ). Both sets are then used as input to hard (deterministic) and soft (probabilistic) discriminators, providing a wide selection of combinations. Moreover, the features can be enriched by using additional information provided by the PLS model. A total of 18 different strategies are considered, including 8 based on Y ‐features and 10 based on T ‐features. Two data sets are used to illustrate the theory, yielding the following results. Both the T ‐ and the Y ‐based approaches are comparable; additional information is ineffective. The cons and pros of each strategy are discussed.