Title
Inter-Iteration Optimization of Parallel EM Algorithm on Message-Passing Multicomputers
Abstract
Estimation of the parameters of a probability distribution function is a complicated problem that is frequently encountered in many instances of real world problems. The Expectation Maximization (EM) algorithm open can be employed when there is a many-to-one mapping from om all possible distribution patterns to the distribution governing the outcome. With its Maximum Likelihood (ML)formulation, optimal estimate can be made for the unknown variables after iterations until convergence. A variety of parallel methods have been proposed to boost its performance because of the complexity involved in the algorithm. Despite the efforts, the ML algorithm could not be easily adopted in practice primarily due to both intra- and inter-iteration data dependence problems resulting from om the iterative nature of the algorithm. This research builds upon experimentation that demonstrated promising results in speeding up the algorithm in and between iterations using distributed-memory message-passing architecture.
Year
DOI
Venue
1998
10.1109/ICPP.1998.708492
ICPP
Keywords
Field
DocType
message-passing multicomputers,inter-iteration optimization,parallel em algorithm,probability density function,convergence,expectation maximization algorithm,complexity,distributed memory,optimal estimation,em algorithm,parallel algorithms,computational complexity,parameter estimation,computer science,message passing,expectation maximization,probability distribution function,maximum likelihood estimation,maximum likelihood,probability distribution
Convergence (routing),Mathematical optimization,Parallel algorithm,Expectation–maximization algorithm,Computer science,Parallel computing,Distributed memory,Population-based incremental learning,Probability density function,Message passing,Computational complexity theory,Distributed computing
Conference
ISSN
ISBN
Citations 
0190-3918
0-8186-8650-2
2
PageRank 
References 
Authors
0.49
4
2
Name
Order
Citations
PageRank
Wei-Min Jeng132.21
Shou-hsuan Stephen Huang217459.88