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 by Thomas

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-superv...

Variable Selection and Updating in Model-based Discriminant Analysis for High Dimensional Data with Food Authenticity

Variable Selection and Updating In Model-Based Discriminant Analysis for High-Dimensional Data

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 datasets with more variables than observations. The variables selected by the proposed method ...

Supervised Feature Extraction of Face Images for Improvement of Recognition Accuracy

Dimensionality reduction methods transform or select a low dimensional feature space to efficiently represent the original high dimensional feature space of data. Feature reduction techniques are an important step in many pattern recognition problems in different fields especially in analyzing of high dimensional data. Hyperspectral images are acquired by remote sensors and human face images ar...

Using Unlabelled Data To Update Classification Rules With Applications In Food Authenticity Studies

A classification method is developed to classify samples when both labelled and unlabelled samples are available. The classification rule is estimated using both the labelled and unlabelled data, in contrast to many classical methods which only use the labelled data for estimation. This methodology models the data as arising from a Gaussian mixture model with parsimonious covariance structure, ...