面向贝叶斯非参数潜在块模型的分布式MCMC推断
Distributed MCMC inference for Bayesian Non-Parametric Latent Block Model
AI总结：
本研究提出采用主从架构的DisNPLBM分布式MCMC推断方法，通过潜在多元高斯块分布实现非参数联合聚类，经实验验证在聚类准确率和效率上表现良好，还可应用于基因表达数据联合聚类且代码开源。
摘要：
本文提出一种面向贝叶斯非参数潜在块模型（DisNPLBM）的新型分布式马尔可夫链蒙特卡罗（MCMC）推断方法，采用主从（Master/Worker）架构。我们的非参数联合聚类算法利用潜在多元高斯块分布，将观测值和特征划分为不同分区。行上的工作负载均匀分配给各工作节点，工作节点仅与主节点通信，彼此间不通信。实验结果表明DisNPLBM在聚类标记准确率和运行时间上均有良好表现。此外，我们展示了将该方法用于基因表达数据联合聚类的实际用例。代码开源在https://github.com/redakhoufache/Distributed-NPLBM。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
