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

Files in This Item:
File Description SizeFormat 
varselect-headlongonly-final.pdf456.48 kBAdobe PDFDownload
Title: Variable selection and updating in model-based discriminant analysis for high dimensional data with food authenticity applications
Authors: Murphy, Thomas BrendanDean, NemaRaftery, Adrian E.
Permanent link:
Date: Mar-2010
Online since: 2011-03-31T10:24:57Z
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.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: Institute of Mathematical Statistics
Journal: Annals of Applied Statistics
Volume: 4
Issue: 1
Start page: 396
End page: 421
Copyright (published version): 2010 The Institute of Mathematical Statistics
Keywords: Food authenticity studiesHeadlong searchModel-based discriminant analysisNormal mixture modelsSemi-supervised learningUpdating classification rulesVariable selection
Subject LCSH: Discriminant analysis
Food law and legislation
DOI: 10.1214/09-AOAS279
Other versions:
Language: en
Status of Item: Peer reviewed
Appears in Collections:Mathematics and Statistics Research Collection

Show full item record

Citations 20

Last Week
Last month
checked on Sep 11, 2020

Page view(s)

Last Week
Last month
checked on Sep 26, 2020

Download(s) 50

checked on Sep 26, 2020

Google ScholarTM



This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.