Quadratic discriminant analysis (QDA) is a widely used statistical tool to classify observations from different multivariate Normal populations. The generalized quadratic (GQDA) classification rule/classifier, which generalizes the QDA and minimum Mahalanobis distance (MMD) classifiers discriminate between populations with underlying elliptically symmetric distributions competes quite favorably...

Summary Functional linear discriminant analysis provides a simple yet efficient method for classification, with the possibility of achieving perfect classification. Several methods have been proposed in literature that mostly address dimensionality problem. On other hand, there is growing interest interpretability analysis, which favours and sparse solution. In this paper we propose new approac...

The objective of this work is to devise an approach for measuring similarity of low-level content features in multi-descriptor spaces. Since no single descriptor is able to represent all the properties and patterns encapsulated in natural images, the combination of several descriptors appears to be a sensible strategy to increase their discrimination power and classification properties. We are ...

The goal of discriminant analysis is to obtain rules that describe the separation between groups of observations. Moreover it allows to classify new observations into one of the known groups. In the classical approach discriminant rules are often based on the empirical mean and covariance matrix of the data, or of parts of the data. But because these estimates are highly influenced by outlying ...