@article{Ahmadzadeh_Manouchehri_Ennajari_Bouguila_Amayri_Fan_2021, title={Entropy-based Variational Learning of Finite Inverted Beta-Liouville Mixture Model}, volume={34}, url={https://journals.flvc.org/FLAIRS/article/view/128379}, DOI={10.32473/flairs.v34i1.128379}, abstractNote={<p>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.</p>}, journal={The International FLAIRS Conference Proceedings}, author={Ahmadzadeh, Mohammad Sadegh and Manouchehri, Narges and Ennajari, Hafsa and Bouguila, Nizar and Amayri, Manar and Fan, Wentao}, year={2021}, month={Apr.} }