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Activity Number:
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460
- Clustering Methods for Big Data Problems
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 2, 2017 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #323326
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View Presentation
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Title:
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A Parallel EM Algorithm for Statistical Learning via Mixture Models
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Author(s):
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Geoffrey McLachlan*
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Companies:
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The University of Queensland
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Keywords:
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mixture models ;
EM algorithm ;
skew distributions ;
multithreaded programs
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Abstract:
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Finite mixtures of skew normal and skew t-distributions provide a flexible tool for modelling 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. We therefore propose a block implementation of the EM algorithm that facilitates the calculations in the E- and M-steps to be spread across a large number of threads. The approach can be easily implemented for use by multicore and multiprocessor machines. The improvement in time performance is illustrated on some real datasets.
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Authors who are presenting talks have a * after their name.
