Monthly
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6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

June 1, 2002, Vol. 14, No. 6, Pages 1261-1266
(doi: 10.1162/089976602753712927)
© 2002 Massachusetts Institute of Technology
SMEM Algorithm Is Not Fully Compatible with Maximum-Likelihood Framework
Article PDF (117.95 KB)
Abstract

The expectation-maximization (EM) algorithm with split-and-merge operations (SMEM algorithm) proposed by Ueda, Nakano, Ghahramani, and Hinton (2000) is a nonlocal searching method, applicable to mixture models, for relaxing the local optimum property of the EM algorithm. In this article, we point out that the SMEM algorithm uses the acceptance-rejection evaluation method, which may pick up a distribution with smaller likelihood, and demonstrate that an increase in likelihood can then be guaranteed only by comparing log likelihoods.