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Multi‐node Expectation–Maximization algorithm for finite mixture models
Author(s) -
Lee Sharon X.,
McLachlan Geoffrey J.,
Leemaqz Kaleb L.
Publication year - 2021
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.11529
Subject(s) - expectation–maximization algorithm , computer science , matlab , maximization , node (physics) , algorithm , maximum likelihood , mixture model , estimation theory , mathematical optimization , artificial intelligence , mathematics , statistics , structural engineering , engineering , operating system
Finite mixture models are powerful tools for modeling 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 in 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.