EM Algorithm with Split and Merge Operations for Mixture Models(Excellent Paper Originally Published in Japanese)

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概要

• 論文の詳細を見る

• The maximum likelihood estimate of a mixture model is usually found by using the EM algorithm. However, the EM algorithm suffers from a local optima problem and therefore we cannot obtain the potential performance of mixture models in practice. In the case of mixture models, local maxima often have too many components of a mixture model in one part of the space and too few in another, widely separated part of the space. To escape from such configurations we proposed a new variant of the EM algorithm in which simultaneous split and merge operations are repeatedly performed by using a new criterion for efficiently selecting the split and merge candidates. We apply the proposed algorithm to the training of Gaussian mixtures and the dimensionality reduction based on a mixture of factor analyzers using synthetic and real data and show that the proposed algorithm can markedly improve the ML estimates.

• 2000-12-25

著者

• Nakano Ryohei

  Ntt Communication Science Laboratories:(present Address)nagoya Institute Of Technology

• UEDA Naonori

  NTT Communication Science Laboratories

関連論文

• Bayesian model search for mixture models based on optimizing variational bounds
• Selection of Shared-State Hidden Markov Model Structure Using Bayesian Criterion(the 2003 IEICE Excellent Paper Award)
• EM Algorithm with Split and Merge Operations for Mixture Models(Excellent Paper Originally Published in Japanese)
• Modeling of growing networks with directional attachment and communities

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