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Abstract:
We present a split and merge EM (SMEM) algorithm to overcome
the local maximum problem in parameter estimation of finite mixture
models. In the case of mixture models, non-global maxima often
involve having 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 repeatedly perform
simultaneous split and merge operations using a new criterion for
efficiently selecting the split and merge candidates. We apply the
proposed algorithm to the training of Gaussian mixtures and
mixtures of factor analyzers using synthetic and real data and show
the effectiveness of using the split and merge operations to
improve the likelihood of both the training data and of held-out
test data.
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