Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/118109
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Type: Journal article
Title: A block EM algorithm for multivariate skew normal and skew t-mixture models
Author: Lee, S.X.
Leemaqz, K.L.
McLachlan, G.J.
Citation: IEEE Transactions on Neural Networks and Learning Systems, 2018; 29(11):5581-5591
Publisher: IEEE
Issue Date: 2018
ISSN: 2162-237X
2162-2388
Statement of
Responsibility: 
Sharon X. Lee, Kaleb L. Leemaqz and Geoffrey J. McLachlan
Abstract: Finite mixtures of skew distributions provide a flexible tool for modeling heterogeneous data with asymmetric distributional features. However, parameter estimation via the Expectation-Maximization (EM) algorithm can become very time consuming due to the complicated expressions involved in the E-step that are numerically expensive to evaluate. While parallelizing the EM algorithm can offer considerable speedup in time performance, current implementations focus almost exclusively on distributed platforms. In this paper, we consider instead the most typical operating environment for users of mixture models-a standalone multicore machine and the R programming environment. We develop a block implementation of the EM algorithm that facilitates the calculations on the E- and M-steps to be spread across a number of threads. We focus on the fitting of finite mixtures of multivariate skew normal and skew distributions, and show that both the E- and M-steps in the EM algorithm can be modified to allow the data to be split into blocks. Our approach is easy to implement and provides immediate benefits to users of multicore machines. Experiments were conducted on two real data sets to demonstrate the effectiveness of the proposed approach.
Keywords: Expectation–Maximization (EM) algorithm; mixture models; parallel algorithm; skew distributions
Rights: © 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
RMID: 0030107864
DOI: 10.1109/TNNLS.2018.2805317
Appears in Collections:Medicine publications

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