mlsanityData to 14 Oct 2026

Hierarchical mixture of discriminative Generalized Dirichlet classifiers

arXiv 2405.01778 ↗First posted 2 May 2024cs.LG stat.MLcited by 0

Not on the map. Why?

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.

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

3 of 49 references.

References · 3

  1. 2022Aitchison's Compositional Data Analysis 40 Years On: A Reappraisalcited by 6
  2. 2021Discriminative Multimodal Learning via Conditional Priors in Generative Modelscited by 4
  3. 2015Improved classification for compositional data using the α-transformationcited by 2

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Diffusion Classifiers Understand Compositionality, but Conditions Apply
  2. 2025Evaluating Compositional Generalisation in VLMs and Diffusion Models
  3. 2025Generative or Discriminative? Revisiting Text Classification in the Era of Transformers
  4. 2025Rethinking Data Mixture for Large Language Models: A Comprehensive Survey and New Perspectives
  5. 2025Gaussian mixture models as a proxy for interacting language models
  6. 2026SpectralGCD: Spectral Concept Selection and Cross-modal Representation Learning for Generalized Category Discovery
  7. 2025How Compositional Generalization and Creativity Improve as Diffusion Models are Trained
  8. 2023Your Diffusion Model is Secretly a Zero-Shot Classifier