Distributed MCMC inference for Bayesian Non-Parametric Latent Block Model

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n 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.

Submitted to Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2024). Taipei, Taiwan. 7-10 May 2024.