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In particular, a sophisticated entropy-based variational algorithm is developed to learn the model and optimize its complexity simultaneously. Moreover, a component-splitting principle is investigated, here, to handle the problem of model selection and to prevent over-fitting, which is an added advantage, as it is done within the variational framework. The performance and merits of the proposed framework are evaluated on multiple, real-challenging applications including dynamic textures clustering, objects categorization and human gesture recognition.<\/jats:p>","DOI":"10.3390\/s22010186","type":"journal-article","created":{"date-parts":[[2021,12,28]],"date-time":"2021-12-28T11:55:03Z","timestamp":1640692503000},"page":"186","source":"Crossref","is-referenced-by-count":3,"title":["Entropy-Based Variational Scheme with Component Splitting for the Efficient Learning of Gamma Mixtures"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-6638-7039","authenticated-orcid":false,"given":"Sami","family":"Bourouis","sequence":"first","affiliation":[{"name":"Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia"}]},{"given":"Yogesh","family":"Pawar","sequence":"additional","affiliation":[{"name":"The Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, QC H3G 1T7, Canada"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-7224-7940","authenticated-orcid":false,"given":"Nizar","family":"Bouguila","sequence":"additional","affiliation":[{"name":"The Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, QC H3G 1T7, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,28]]},"reference":[{"key":"ref_1","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bouguila, N., and Fan, W. (2020). 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