论文标题
有限混合模型的多节点EM算法
Multi-Node EM Algorithm for Finite Mixture Models
论文作者
论文摘要
有限混合模型是用于建模和分析异质数据的强大工具。参数估计通常是通过预期最大化（EM）算法使用最大似然估计进行的。最近，采用柔性分布作为组件密度越来越流行。通常，这些模型的EM算法涉及复杂的表达式，这些表达式很耗时以进行数值评估。在本文中，我们描述了适用于单线程和多线程处理器的EM-Algorithm的并行实现，以及单个机器和多节点系统。进行数值实验以证明潜在的性能增益n不同的设置。还可以在两个常用的平台-R和MATLAB上进行比较。为了说明，在比较中使用了相当一般的混合模型。
Finite mixture models are powerful tools for modelling and analyzing heterogeneous data. Parameter estimation is typically carried out using maximum likelihood estimation via the Expectation-Maximization (EM) algorithm. Recently, the adoption of flexible distributions as component densities has become increasingly popular. Often, the EM algorithm for these models involves complicated expressions that are time-consuming to evaluate numerically. In this paper, we describe a parallel implementation of the EM-algorithm suitable for both single-threaded and multi-threaded processors and for both single machine and multiple-node systems. Numerical experiments are performed to demonstrate the potential performance gain n different settings. Comparison is also made across two commonly used platforms - R and MATLAB. For illustration, a fairly general mixture model is used in the comparison.
