Open AccessEntropy-based Variational Learning of Finite Inverted Beta-Liouville Mixture Model
Author(s) -
Mohammad Sadegh Ahmadzadeh,
Narges Manouchehri,
Hafsa Ennajari,
Nizar Bouguila,
Manar Amayri,
Wentao Fan
Publication year - 2021
Publication title -
proceedings of the ... international florida artificial intelligence research society conference
Language(s) - English
Resource type - Journals
eISSN - 2334-0762
pISSN - 2334-0754
DOI - 10.32473/flairs.v34i1.128379
Subject(s) - cluster analysis , inference , entropy (arrow of time) , artificial intelligence , unsupervised learning , mixture model , computer science , categorization , beta (programming language) , pattern recognition (psychology) , algorithm , mathematics , machine learning , physics , programming language , quantum mechanics
Mixture models are a common unsupervised learning technique that have been widely used to statistically approximate and analyse heterogenous data. In this paper, an effective mixture model-based approach for positive vectors clustering and modeling is proposed. Our mixture model is based on the inverted Beta-Liouville (IBL) distribution. To deploy the proposed model, we introduce an entropy-based variational inference algorithm. The performance of the proposed model is evaluated on two real-world applications, namely, human activity recognition and image categorization.