Food authenticity studies are concerned with determining if food
samples have been correctly labeled 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 data sets 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 data sets, our proposed method
outperformed default implementations of Random Forests,
AdaBoost, transductive SVMs and Bayesian Multinomial Regression
by substantial margins.
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