{"type":"ScholarlyArticle","title":"Learning Finite Beta-Liouville Mixture Models via Variational Bayes for Proportional Data Clustering","authors":[{"name":"Wentao Fan","slug":"wentao-fan","given":"Wentao","family":"Fan","affiliation":"Electrical and Computer Engineering Concordia University , Canada"},{"name":"Nizar Bouguila","slug":"nizar-bouguila","given":"Nizar","family":"Bouguila","affiliation":"Institute for Information Systems Engineering Concordia University , Canada"}],"abstract":"During the past decade, finite mixture modeling has become a well-established technique in data analysis and clustering. This paper focus on developing a variational inference framework to learn finite Beta-Liouville mixture models that have been proposed recently as an efficient way for proportional data clustering. In contrast to the conventional expectation maximization (EM) algorithm, commonly used for learning finite mixture models, the proposed algorithm has the advantages that it is more efficient from a computational point of view and by preventing over- and under-fitting problems. Moreover, the complexity of the mixture model (i.e. the number of components) can be determined automatically and simultaneously with the parameters estimation in a closed form as part of the Bayesian inference procedure. The merits of the proposed approach are shown using both artificial data sets and two interesting and challenging real applications namely dynamic textures clustering and facial expression recognition.","doi":null,"url":"https://ijcai.org/proceedings/2013/199","pdf":"https://ijcai.org/proceedings/2013/0199.pdf","markdown":"https://ijcai.org/proceedings/2013/199.md","bibtex":"https://ijcai.org/proceedings/2013/bibtex/199","volume":{"slug":"2013","title":"Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2013","url":"https://ijcai.org/proceedings/2013/","year":2013,"location":"Beijing, China","dates":"3–9 August 2013","editors":"Francesca Rossi","isbn":"978-1-57735-633-2","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":199,"pages":{"first":1323,"last":1328},"keywords":[],"videos":[],"references":[{"position":1,"raw":"[Attias, 1999a] H. 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