The important role of finite mixture models in the statistical analysis of data is underscored
by the ever-increasing rate at which articles on mixture applications appear in the statistical …

The notion of defining a cluster as a component in a mixture model was put forth by
Tiedeman in 1955; since then, the use of mixture models for clustering has grown into an …

Deep learning is a hierarchical inference method formed by subsequent multiple layers of
learning able to more efficiently describe complex relationships. In this work, deep Gaussian …

The use of a finite mixture of normal distributions in model-based clustering allows us to
capture non-Gaussian data clusters. However, identifying the clusters from the normal …

To achieve an insightful clustering of multivariate data, we propose subspace K-means. Its
central idea is to model the centroids and cluster residuals in reduced spaces, which allows …

As data sets continue to grow in size and complexity, effective and efficient techniques are
needed to target important features in the variable space. Many of the variable selection …

Infinite Mixtures of Infinite Factor Analysers Page 1 Bayesian Analysis (2020) 15, Number 3,
pp. 937–963 Infinite Mixtures of Infinite Factor Analysers Keefe Murphy ∗ , Cinzia Viroli † …

In the framework of cluster analysis based on Gaussian mixture models, it is usually
assumed that all the variables provide information about the clustering of the sample units …

The LIPGENE‐SU. VI. MAX study, like many others, recorded high‐dimensional continuous
phenotypic data and categorical genotypic data. LIPGENE‐SU. VI. MAX focuses on the need …

Mixtures of factor analysers (MFA) models represent a popular tool for finding structure in
data, particularly high-dimensional data. While in most applications the number of clusters …