Entropy-based variational Bayes learning framework for data clustering<?show [AQ ID=Q1]?>

A novel framework is developed for the modelling and clustering of proportional data (i.e. normalised histograms) based on the Beta-Liouville mixture model. This framework is based on incremental model selection, by testing if a given component was truly Beta-Liouville distributed. Specifically, the authors compare the theoretical maximum entropy of the given component with the estimated entropy obtained by the MeanNN estimator. If a significant difference was gained from this comparison, this component is considered as not well fitted and is then splitted into two new components with a proper initialisation. Our approach is tested through synthetic data sets and real-world applications which involve human gesture recognition and vehicle tracking for traffic monitoring purposes, which demonstrate that the authors' approach is superior to comparable techniques.