OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification
2006 (Swedish)In: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 20, no 8-10, 341-351 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
2006. Vol. 20, no 8-10, 341-351 p.
OPLS-DA, orthogonal, multivariate, classification, PLS-DA, SIMCA
IdentifiersURN: urn:nbn:se:umu:diva-13140DOI: doi:10.1002/cem.1006OAI: oai:DiVA.org:umu-13140DiVA: diva2:152811