OPLS discriminant analysis: combining the strengths of PLS‐DA and SIMCA classification

TLDR

The OPLS method’s characteristics have been examined for discriminant analysis (OPLS‑DA). The study demonstrates how exploiting class‑orthogonal variation can enhance classification performance and outlines a strategy that combines PLS‑DA and SIMCA strengths within OPLS‑DA. This is achieved by applying OPLS‑DA to separate orthogonal variation, then using resampling to generate predicted classification distributions and assess classification belief. When class‑orthogonal variation is absent, predictions match traditional PLS‑DA; the method also enables statistically sound use of this variation and yields a decision rule that is comparable to or less class‑biased than common rules. © 2007 John Wiley & Sons, Ltd.

Abstract

Abstract The characteristics of the OPLS method have been investigated for the purpose of discriminant analysis (OPLS‐DA). We demonstrate how class‐orthogonal variation can be exploited to augment classification performance in cases where the individual classes exhibit divergence in within‐class variation, in analogy with soft independent modelling of class analogy (SIMCA) classification. The prediction results will be largely equivalent to traditional supervised classification using PLS‐DA if no such variation is present in the classes. A discriminatory strategy is thus outlined, combining the strengths of PLS‐DA and SIMCA classification within the framework of the OPLS‐DA method. Furthermore, resampling methods have been employed to generate distributions of predicted classification results and subsequently assess classification belief. This enables utilisation of the class‐orthogonal variation in a proper statistical context. The proposed decision rule is compared to common decision rules and is shown to produce comparable or less class‐biased classification results. Copyright © 2007 John Wiley & Sons, Ltd.