Hierarchical mixture of discriminative Generalized Dirichlet classifiers
This work addresses classification challenges for compositional data in specific domains like spam detection, but it appears incremental as it builds on existing Generalized Dirichlet and mixture of experts frameworks.
The paper tackles the problem of classifying compositional data by proposing a discriminative classifier based on the Generalized Dirichlet distribution and extending it into a hierarchical mixture model, with experimental results showing applications in spam detection and color space identification.
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.