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

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

2009-10-14

arxiv.org/abs/0910.2585v2

Annals of Applied Statistics 2010, Vol. 4, No. 1, 396-421

Statistics

Methodology

Published in at http://dx.doi.org/10.1214/09-AOAS279 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Ins

Scientific paper

10.1214/09-AOAS279

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.

Affiliated with

Also associated with

No associations

Rating

Variable selection and updating in model-based discriminant analysis for high dimensional data with food authenticity applications does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Variable selection and updating in model-based discriminant analysis for high dimensional data with food authenticity applications, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Variable selection and updating in model-based discriminant analysis for high dimensional data with food authenticity applications will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-18961