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A Fully Bayesian Framework for Positive Data Clustering

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Artificial Intelligence Applications in Information and Communication Technologies

Part of the book series: Studies in Computational Intelligence ((SCI,volume 607))

Abstract

The main concern with mixture modeling is to describe data in which each observation belongs to one of some number of different groups. Mixtures of distributions provide a flexible and convenient class of models for density estimation and their statistical learning has been studied extensively. In this context, fully Bayesian approaches have been widely adopted for mixture estimation and model selection problems and have shown some effectiveness due to the incorporation of the prior knowledge about the parameters. In this chapter, we propose a fully Bayesian approach for finite generalized Inverted Dirichlet (GID) mixture model learning using a reversible jump Markov chain Monte Carlo (RJMCMC) approach [23]. RJMCMC enables us to deal simultaneously with model selection and parameters estimation in one single algorithm. The merits of RJMCMC for GID mixture learning is investigated using synthetic data and a real interesting application namely object detection.

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Notes

  1. 1.

    http://cogcomp.cs.illinois.edu/Data/Car/.

  2. 2.

    http://pascal.inrialpes.fr/data/human/.

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Acknowledgments

The completion of this research was made possible thanks to the Natural Sciences and Engineering Research Council of Canada (NSERC).

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada

    Mohamed Al Mashrgy

  2. Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada

    Nizar Bouguila

Authors
  1. Mohamed Al Mashrgy
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  2. Nizar Bouguila
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Correspondence to Nizar Bouguila .

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Editors and Affiliations

  1. Taif University, Taif, Saudi Arabia

    Yacine Laalaoui

  2. Faculty of Engineering and Computer Science, Concordia Univ.,Concordia Institute for Information Systems Engineering, Montreal, Québec, Canada

    Nizar Bouguila

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Mashrgy, M.A., Bouguila, N. (2015). A Fully Bayesian Framework for Positive Data Clustering. In: Laalaoui, Y., Bouguila, N. (eds) Artificial Intelligence Applications in Information and Communication Technologies. Studies in Computational Intelligence, vol 607. Springer, Cham. https://doi.org/10.1007/978-3-319-19833-0_7

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  • DOI: https://doi.org/10.1007/978-3-319-19833-0_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19832-3

  • Online ISBN: 978-3-319-19833-0

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