# Hierarchical mixture of discriminative Generalized Dirichlet classifiers

arXiv: https://arxiv.org/abs/2405.01778 · HTML: https://mlsanity.com/paper/2405.01778/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 49 references that are in the map.
- First posted on arXiv 2 May 2024.

## Builds on (most cited first, 3 of 3)

- [Aitchison's Compositional Data Analysis 40 Years On: A Reappraisal](https://mlsanity.com/paper/2201.05197/) (2022)
- [Discriminative Multimodal Learning via Conditional Priors in Generative Models](https://mlsanity.com/paper/2110.04616/) (2021)
- [Improved classification for compositional data using the α-transformation](https://mlsanity.com/paper/1506.04976/) (2015)

## Abstract (arXiv)

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.