Unsupervised Learning Using Expectation Propagation Inference of Inverted Beta-Liouville Mixture Models for Pattern Recognition Applications. Issue 4 (19th May 2023)

• Record Type: Journal Article
• Authors: Bourouis, Sami; Bouguila, Nizar
• Is Part Of: Cybernetics and systems. Volume 54:Issue 4(2023)
• Page Start: 474
• Page End: 498
• Publication Date: 2023-05-19
• Subjects: Action recognition -- expectation propagation -- facial recognition -- hand-gesture recognition -- inverted Beta-Liouville distribution -- mixture models; Cybernetics -- Periodicals; System theory -- Periodicals; 003.5
• DOI: 10.1080/01969722.2022.2062850
• Languages: English
• ISSNs: 0196-9722

Abstract

Abstract: Learning statistical models successfully is both an essential and a challenging task for various pattern recognition and knowledge discovery applications. In particular, generative models such as finite and infinite mixture models have demonstrated to be efficient in terms of overall performance. In this paper, a robust framework based on an expectation propagation (EP) inference is developed to learn inverted Beta-Liouville (IBL) mixture models which is proper choice for positive data classification. Within the proposed EP learning method, the full posterior distribution is estimated accurately, the model complexity and all related parameters are evaluated simultaneously in a single optimization scheme. Extensive experiments using challenging real-world applications including recognition of facial expression, automatic human action categorization, and hand gesture recognition show the merit of our approach in terms of achieving better results than comparable techniques.