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

Journal article

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 and Sons, Ltd.

Authors: Bylesjo, M; Rantalainen, M; Cloarec, O; Nicholson, J; Holmes, E

• Publication status: Published
• Journal: JOURNAL OF CHEMOMETRICS
• Volume: 20
• Issue: 8-10
• Pages: 341-351
• Publication date: 2006-01-01
• DOI: 10.1002/cem.1006
• EISSN: 1099-128X
• ISSN: 0886-9383
• Language: English
• Keywords: SIMCA; multivariate; orthogonal; classification; PLS-DA; OPLS-DA