Entropy-based Variational Learning of Finite Inverted Beta-Liouville Mixture Model

Mohammad Sadegh Ahmadzadeh, Narges Manouchehri, Hafsa Ennajari, Nizar Bouguila, Manar Amayri, Wentao Fan

Abstract: 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.