Entropy‐based variational Bayes learning framework for data clustering
Wentao Fan, Nizar Bouguila, Sami Bourouis, Yacine Laalaoui · IET Image Processing · 2018
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.