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- W4200412787 abstract "Mixture models become increasingly popular due to their modeling flexibility and are applied to the clustering and classification of heterogeneous data. The EM algorithm is largely used for the maximum likelihood estimation of mixture models because the algorithm is stable in convergence and simple in implementation. Despite such advantages, it is pointed out that the EM algorithm is local and has slow convergence as the main drawback. To avoid the local convergence of the EM algorithm, multiple runs from several different initial values are usually used. Then the algorithm may take a large number of iterations and long computation time to find the maximum likelihood estimates. The speedup of computation of the EM algorithm is available for these problems. We give the algorithms to accelerate the convergence of the EM algorithm and apply them to mixture model estimation. Numerical experiments examine the performance of the acceleration algorithms in terms of the number of iterations and computation time." @default.
- W4200412787 created "2021-12-31" @default.
- W4200412787 creator A5063851579 @default.
- W4200412787 date "2021-12-08" @default.
- W4200412787 modified "2023-09-27" @default.
- W4200412787 title "Fast Computation of the EM Algorithm for Mixture Models" @default.
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- W4200412787 doi "https://doi.org/10.5772/intechopen.101249" @default.
- W4200412787 hasPublicationYear "2021" @default.
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