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Expectation propagation learning of finite and infinite Gamma mixture models and its applications

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Abstract

In this paper, we propose an efficient learning framework for both finite and infinite Gamma mixture models. Unlike existing learning methods such as maximum-likelihood method, we propose here to tackle the problem of learning Gamma mixtures within a coherent and unified framework based on an expectation-propagation inference method. In addition, we introduce an effective Bayesian technique in order to generalize the finite Gamma (EP-GaMM) to the infinite mixture (EP-inGaMM). The developed framework offers accurate approximations to the full posterior and takes into account the prior knowledge in the statistical model. In particular, the model’s parameters are estimated accurately and the optimal number of components is determined automatically. The choice of Gamma mixture is motivated by its flexibility and its modeling capabilities in solving many real-life applications. We highlight the importance of the proposed framework by solving main common spatio-temporal objects recognition challenges which might be used especially in interactive systems or robotics. The effectiveness of our approach is demonstrated through several real challenging applications. Our experiments shows comparable to superior results to other methods from the state-of-the-art. Results show that the average recognition accuracy of the proposed framework can achieve 78% which is better than many other related methods.

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  1. https://labicvl.github.io/gesdb.htm

  2. https://labicvl.github.io/gesdb.htm

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Acknowledgement

The researchers would like to acknowledge Deanship of Scientific Research, Taif University for funding this work.

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  1. Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia

    Sami Bourouis

  2. The Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, QC H3G 1T7, Canada

    Nizar Bouguila

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  1. Sami Bourouis
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Correspondence to Sami Bourouis.

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Bourouis, S., Bouguila, N. Expectation propagation learning of finite and infinite Gamma mixture models and its applications. Multimed Tools Appl 82, 33267–33284 (2023). https://doi.org/10.1007/s11042-023-14666-w

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