Variable selection and updating in model-based discriminant analysis for high dimensional data with food authenticity applications

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dc.contributor.author Murphy, Thomas Brendan
dc.contributor.author Dean, Nema
dc.contributor.author Raftery, Adrian E.
dc.date.accessioned 2011-03-31T10:24:57Z
dc.date.available 2011-03-31T10:24:57Z
dc.date.copyright 2010 The Institute of Mathematical Statistics en
dc.date.issued 2010-03
dc.identifier.citation Annals of Applied Statistics en
dc.identifier.uri http://hdl.handle.net/10197/2884
dc.description.abstract Food authenticity studies are concerned with determining if food samples have been correctly labelled or not. Discriminant analysis methods are an integral part of the methodology for food authentication. Motivated by food authenticity applications, a model-based discriminant analysis method that includes variable selection is presented. The discriminant analysis model is fitted in a semi-supervised manner using both labeled and unlabeled data. The method is shown to give excellent classification performance on several high-dimensional multiclass food authenticity datasets with more variables than observations. The variables selected by the proposed method provide information about which variables are meaningful for classification purposes. A headlong search strategy for variable selection is shown to be efficient in terms of computation and achieves excellent classification performance. In applications to several food authenticity datasets, our proposed method outperformed default implementations of Random Forests, AdaBoost, transductive SVMs and Bayesian Multinomial Regression by substantial margins. en
dc.description.sponsorship Science Foundation Ireland en
dc.format.extent 467433 bytes
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Institute of Mathematical Statistics en
dc.subject Food authenticity studies en
dc.subject Headlong search en
dc.subject Model-based discriminant analysis en
dc.subject Normal mixture models en
dc.subject Semi-supervised learning en
dc.subject Updating classification rules en
dc.subject Variable selection en
dc.subject.lcsh Discriminant analysis en
dc.subject.lcsh Food law and legislation en
dc.subject.lcsh Food--Labeling en
dc.title Variable selection and updating in model-based discriminant analysis for high dimensional data with food authenticity applications en
dc.type Journal Article en
dc.internal.availability Full text available en
dc.internal.webversions Publisher's version en
dc.internal.webversions http://projecteuclid.org/euclid.aoas/1273584460 en
dc.status Peer reviewed en
dc.identifier.volume 4 en
dc.identifier.issue 1 en
dc.identifier.startpage 396 en
dc.identifier.endpage 421 en
dc.identifier.doi 10.1214/09-AOAS279
dc.neeo.contributor Murphy|Thomas Brendan|aut| en
dc.neeo.contributor Dean|Nema|aut| en
dc.neeo.contributor Raftery|Adrian E.|aut| en


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