Clustering is the task of automatically gathering observations into homogeneous groups,
where the number of groups is unknown. Through its basis in a statistical modeling …

Task-parameterized models of movements aim at automatically adapting movements to new
situations encountered by a robot. The task parameters can, for example, take the form of …

Abstract Model-based clustering is a popular tool which is renowned for its probabilistic
foundations and its flexibility. However, high-dimensional data are nowadays more and …

The prominence of finite mixture modelling is greater than ever. Many important statistical
topics like clustering data, outlier treatment, or dealing with unobserved heterogeneity …

We consider the problem of clustering observations using a potentially large set of features.
One might expect that the true underlying clusters present in the data differ only with respect …

Cluster analysis faces two problems in high dimensions: the “curse of dimensionality” that
can lead to overfitting and poor generalization performance and the sheer time taken for …

Latent variable models exist with continuous, categorical, or both types of latent variables.
The role of latent variables is to account for systematic patterns in the observed responses …

Clustering in high-dimensional spaces is a difficult problem which is recurrent in many
domains, for example in image analysis. The difficulty is due to the fact that high …

Normal mixture models are widely used for statistical modeling of data, including cluster
analysis. However maximum likelihood estimation (MLE) for normal mixtures using the EM …

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 …