  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.
