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• W3100220322 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." @default.
• W3100220322 created "2020-11-23" @default.
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• W3100220322 date "2010-03-01" @default.
• W3100220322 modified "2023-09-27" @default.
• W3100220322 title "Variable selection and updating in model-based discriminant analysis for high dimensional data with food authenticity applications" @default.
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• W3100220322 doi "https://doi.org/10.1214/09-aoas279" @default.
• W3100220322 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/20936055" @default.
• W3100220322 hasPublicationYear "2010" @default.
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