Clustering in high-dimensions poses many statistical challenges. While traditional distance-based clustering methods are computationally feasible, they lack probabilistic interpretation and rely on heuristics for estimation of the number clusters. On other hand, model-based techniques often fail to scale devising algorithms that able effectively explore posterior space is an open problem. Based...

The problem of clustering, or unsupervised classification, has been solved by a myriad of techniques, all of which depend, either directly or implicitly, on the Bayesian principle of optimal classification. To be more specific, within a Bayesian paradigm, if one is to compare the testing sample with only a single point in the feature space from each class, the optimal Bayesian strategy would be...

Bayesian models offer great flexibility for clustering applications—Bayesian nonparametrics can be used for modeling infinite mixtures, and hierarchical Bayesian models can be utilized for sharing clusters across multiple data sets. For the most part, such flexibility is lacking in classical clustering methods such as k-means. In this paper, we revisit the k-means clustering algorithm from a Ba...

Automatic clustering of utterances can be useful for the modeling of dialogue acts for dialogue applications. Previously, the Chinese restaurant process (CRP), a non-parametric Bayesian method, has been introduced and has shown promising results for the clustering of utterances in dialogue. This paper introduces the infinite HMM, which is also a non-parametric Bayesian method, and verifies its ...

Moroccan goats are characterized by the presence of different populations identified only based on their phenotypes. The objectives of this study were to assess the genetic differentiation of the Draa goat breed and to analyze its genetic structure and its relationships with other local populations using 12 microsatellite markers. The screening was done in South Eastern and Southern Morocco on ...