The Annals of Applied Statistics

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

Thomas Brendan Murphy, Nema Dean, and Adrian E. Raftery

Full-text: Open access

Abstract

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.

Article information

Source
Ann. Appl. Stat. Volume 4, Number 1 (2010), 396-421.

Dates
First available: 11 May 2010

Permanent link to this document
http://projecteuclid.org/euclid.aoas/1273584460

Digital Object Identifier
doi:10.1214/09-AOAS279

Zentralblatt MATH identifier
1189.62105

Mathematical Reviews number (MathSciNet)
MR2758177

Citation

Murphy, Thomas Brendan; Dean, Nema; Raftery, Adrian E. Variable selection and updating in model-based discriminant analysis for high dimensional data with food authenticity applications. The Annals of Applied Statistics 4 (2010), no. 1, 396--421. doi:10.1214/09-AOAS279. http://projecteuclid.org/euclid.aoas/1273584460.


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