Hierarchical mixture of discriminative Generalized Dirichlet classifiers

TL;DR

This paper introduces a hierarchical discriminative classifier for compositional data based on the Generalized Dirichlet distribution, utilizing a novel variational bound for parameter learning, with applications in spam detection and color identification.

Contribution

It proposes the first variational upper-bound for the Generalized Dirichlet mixture and develops a hierarchical mixture of discriminative classifiers for compositional data.

Findings

Effective in spam detection tasks

Successful in color space identification

First to derive a variational bound for this model

Abstract

This paper presents a discriminative classifier for compositional data. This classifier is based on the posterior distribution of the Generalized Dirichlet which is the discriminative counterpart of Generalized Dirichlet mixture model. Moreover, following the mixture of experts paradigm, we proposed a hierarchical mixture of this classifier. In order to learn the models parameters, we use a variational approximation by deriving an upper-bound for the Generalized Dirichlet mixture. To the best of our knownledge, this is the first time this bound is proposed in the literature. Experimental results are presented for spam detection and color space identification.