In this paper, we develop a novel variational Bayesian learning method for the Dirichlet
process (DP) mixture of the inverted Dirichlet distributions, which has been shown to be very …

We developed a variational Bayesian learning framework for the infinite generalized
Dirichlet mixture model (ie a weighted mixture of Dirichlet process priors based on the …

In statistical modeling, parameter estimation is an essential and challengeable task.
Estimation of the parameters in the Dirichlet mixture model (DMM) is analytically intractable …

In this paper, we focus on the variational learning of finite Dirichlet mixture models.
Compared to other algorithms that are commonly used for mixture models (such as …

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 …

This paper addresses the Bayesian estimation of the von-Mises Fisher (vMF) mixture model
with variational inference (VI). The learning task in VI consists of optimization of the …

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 …

In this paper, we present an incremental method for model selection and learning of
Gaussian mixtures based on the recently proposed variational Bayes approach. The method …

Finite mixture models have been widely used for modeling probability distribution of real
data sets due to its benefits from analytical tractability. Among the finite mixtures, the finite …