This paper presents an unsupervised approach for feature selection and extraction in
mixtures of generalized Dirichlet (GD) distributions. Our method defines a new mixture …

The Dirichlet distribution is a multivariate generalization of the Beta distribution. It is an
important multivariate continuous distribution in probability and statistics. In this report, we …

The work proposed in this paper is motivated by the need to develop powerful models and
approaches to classify and learn proportional data. Indeed, an abundance of interesting …

In this paper, we examine the problem of count data clustering. We analyze this problem
using finite mixtures of distributions. The multinomial distribution and the multinomial …

In this paper, we propose a clustering algorithm based on both Dirichlet processes and
generalized Dirichlet distribution which has been shown to be very flexible for proportional …

We describe approaches for positive data modeling and classification using both finite
inverted Dirichlet mixture models and support vector machines (SVMs). Inverted Dirichlet …

In this paper, a novel statistical generative model based on hierarchical Pitman-Yor process
and generalized Dirichlet distributions (GDs) is presented. The proposed model allows us to …

This paper introduces a novel enhancement for unsupervised feature selection based on
generalized Dirichlet (GD) mixture models. Our proposal is based on the extension of the …

Mixture modeling is one of the most useful tools in machine learning and data mining
applications. An important challenge when applying finite mixture models is the selection of …

Finite mixture models are one of the most widely and commonly used probabilistic
techniques for image segmentation. Although the most well known and commonly used …