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 …

Cluster analysis finds groups in data automatically. Most methods have been heuristic and
leave open such central questions as: how many clusters are there? Which method should I …

Modelling based on finite mixture distributions is a rapidly developing area with the range of
applications exploding. Finite mixture models are nowadays applied in such diverse areas …

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 …

Variable selection in clustering analysis is both challenging and important. In the context of
modelbased clustering analysis with a common diagonal covariance matrix, which is …