Open AccessEntropy‐based variational Bayes learning framework for data clustering
Author(s) -
Fan Wentao,
Bouguila Nizar,
Bourouis Sami,
Laalaoui Yacine
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.0043
Subject(s) - cluster analysis , computer science , entropy (arrow of time) , histogram , artificial intelligence , estimator , pattern recognition (psychology) , component (thermodynamics) , model selection , data mining , joint entropy , machine learning , principle of maximum entropy , mathematics , statistics , image (mathematics) , physics , quantum mechanics , thermodynamics
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.