%0 Journal Article %1 Bylesjo2006 %A Bylesjø, Max %A Rantalainen, Mattias %A Cloarec, Olivier %A Nicholson, Jeremy K. %A Holmes, Elaine %A Tryg, Johan %C Research Group for Chemometrics, Department of Chemistry, Ume� University, Ume� SE-901 87, Sweden; Biological Chemistry, Biomedical Sciences Division, Faculty of Natural Sciences, Imperial College London, South Kensington SW7 2AZ, UK %D 2006 %I Wiley %J Journal of Chemometrics %K chemometrics classification pls %N 8-10 %P 341-351 %R 10.1002/cem.1006 %T OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification %U http://dx.doi.org/10.1002/cem.1006 %V 20 %X 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.

@article{Bylesjo2006, 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.}, added-at = {2010-09-28T03:07:59.000+0200}, address = {Research Group for Chemometrics, Department of Chemistry, Ume� University, Ume� SE-901 87, Sweden; Biological Chemistry, Biomedical Sciences Division, Faculty of Natural Sciences, Imperial College London, South Kensington SW7 2AZ, UK}, author = {Bylesj\o, Max and Rantalainen, Mattias and Cloarec, Olivier and Nicholson, Jeremy K. and Holmes, Elaine and Tryg, Johan}, biburl = {https://www.bibsonomy.org/bibtex/252e03bca71d42a9210e0b42a9f9ba11b/afcallender}, description = {OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification. Max Bylesjö. 2007; Journal of Chemometrics - Wiley InterScience}, doi = {10.1002/cem.1006}, groups = {public}, interhash = {52492e8a614eb8d72958423681996ca3}, intrahash = {52e03bca71d42a9210e0b42a9f9ba11b}, journal = {Journal of Chemometrics}, keywords = {chemometrics classification pls}, number = {8-10}, pages = {341-351}, publisher = {Wiley}, timestamp = {2012-12-06T23:15:40.000+0100}, title = {OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification}, url = {http://dx.doi.org/10.1002/cem.1006}, username = {afcallender}, volume = 20, year = 2006 }