EFFECTIVE EXPECTATION MAXIMIZATION ALGORITHM IMPLEMENTATION USING MULTICORE COMPUTER SYSTEMS
A popular expectation maximization algorithm that is widely used in modern data processing systems to solve various problems including optimization and parameter estimation is considered in the paper. The task of the study was to enhance effectiveness of the algorithm execution in time. An enhancement of execution rate for the EM algorithm using multicore architecture of modern computer systems was carried out. Necessary modifications aimed at better parallelism were proposed for implementation of the EM algorithm. An efficiency of the software implementation was tested on the classic problem of Gaussian random variables mixture separation. It is shown that in the mixture separation problem EM algorithm performance degrades when the distance between mean values of distributions is less than three standard deviations, which is totally in the spirit of three sigma law. In such cases, it is very important to have an efficient EM algorithm implementation to be able to process such test cases in a reasonable time.
expectation maximization algorithm; multicore architecture; parallelism; problem of Gaussian random variables mixture separation; three sigma law
Ben-Ari M.: Principles of Concurrent and Distributed Programming (2nd ed.). Addison-Wesley, 2006.
Bidyuk P. I., Gozhij O. P., Korshevnyuk L.O.: Computer based decision support systems. Chornomorsky State University named after Petro Mogyla, Mykolaiv, 2012.
Borman S.: The expectation maximization algorithm a short tutorial. http://www.seanborman.com/publications/EM_algorithm.pdf
Chapman B., Jost G. R., Kuck D. J.: Using Open MP: Portable Shared Memory Parallel Programming. The MIT Press, Boston 2007.
Dellaert F.: The expectation maximization algorithm. Techn. paper, Georgia Institute of Technology, 2002.
Dinov I. D.: Expectation maximization and mixture modeling tutorial. http://www.stat.ucla.edu/~dinov/courses_students.dir/04/Spring/Stat233.dir/STAT233_notes.dir/EM_Tutorial.pdf
Hollsapple C. W., Winston A. B.: Decision support systems. West Publishing Company, New York 1996.
Korbicz J.(Ed.): Measurements, models, systems and design. Wydawnictwa Komunikacji i łączności, 2007.
Quinn M. J.: Parallel Programming in C with MPI and Open MP. McGraw-Hill Inc. 2004.
Shuicheng Y., Chang S. F., Johnson M. H., Xi Z., Xiaodan Z., Huang T. S.: Sift-bag kernel for video event analysis. Proceeding of the 16th ACM international conference on Multimedia, 2008, 229-238.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.