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Distributed MCMC Inference for Bayesian Non-parametric Latent Block Model

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Advances in Knowledge Discovery and Data Mining (PAKDD 2024)

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Abstract

In this paper, we introduce a novel Distributed Markov Chain Monte Carlo (MCMC) inference method for the Bayesian Non-Parametric Latent Block Model (DisNPLBM), employing the Master/Worker architecture. Our non-parametric co-clustering algorithm divides observations and features into partitions using latent multivariate Gaussian block distributions. The workload on rows is evenly distributed among workers, who exclusively communicate with the master and not among themselves. DisNPLBM demonstrates its impact on cluster labeling accuracy and execution times through experimental results. Moreover, we present a real-use case applying our approach to co-cluster gene expression data. The code source is publicly available at https://github.com/redakhoufache/Distributed-NPLBM

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Notes

  1. 1.

    https://www.grid5000.fr/.

  2. 2.

    https://www.terraform.io/.

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Acknowledgements

This work has been supported by the Paris Île-de-France Région in the framework of DIM AI4IDF. I thank Grid5000 for providing the essential computational resources and the start-up HephIA for the invaluable exchange on scalable algorithms.

Author information

Authors and Affiliations

  1. Paris-Saclay University, UVSQ, David Lab, 78035, Versailles, France

    Reda Khoufache, Anisse Belhadj & Mustapha Lebbah

  2. LIPN (UMR CNRS 7030), Sorbonne Paris Nord University, Villetaneuse, France

    Hanene Azzag

Authors
  1. Reda Khoufache
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  2. Anisse Belhadj
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  3. Hanene Azzag
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  4. Mustapha Lebbah
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Corresponding author

Correspondence to Reda Khoufache .

Editor information

Editors and Affiliations

  1. Academia Sinica, Taipei, Taiwan

    De-Nian Yang

  2. Microsoft Research Asia, Beijing, China

    Xing Xie

  3. National Yang Ming Chiao Tung University, Hsinchu, Taiwan

    Vincent S. Tseng

  4. Duke University, Durham, NC, USA

    Jian Pei

  5. National Cheng Kung University, Tainan, Taiwan

    Jen-Wei Huang

  6. Silesian University of Technology, Gliwice, Poland

    Jerry Chun-Wei Lin

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Khoufache, R., Belhadj, A., Azzag, H., Lebbah, M. (2024). Distributed MCMC Inference for Bayesian Non-parametric Latent Block Model. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14645. Springer, Singapore. https://doi.org/10.1007/978-981-97-2242-6_22

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  • DOI: https://doi.org/10.1007/978-981-97-2242-6_22

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2241-9

  • Online ISBN: 978-981-97-2242-6

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