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Norio Tagawa
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Journal Articles
Publisher: Journals Gateway
Neural Computation (2002) 14 (6): 1261–1266.
Published: 01 June 2002
Abstract
View articletitled, SMEM Algorithm Is Not Fully Compatible with Maximum-Likelihood Framework
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for article titled, SMEM Algorithm Is Not Fully Compatible with Maximum-Likelihood Framework
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.