Fast Computation of the EM Algorithm for Mixture Models
Fast Computation of the EM Algorithm for Mixture Models
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DOI：
10.5772/intechopen.101249
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
M. Kuroda
中科院分区：
文献类型：
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作者：
M. Kuroda
混合模型由于其建模的灵活性而越来越受到人们的欢迎，并被应用于异构数据的聚类和分类。EM算法因其收敛稳定、实现简单而被广泛应用于混合模型的极大似然估计。尽管有这些优点，但指出EM算法是局部的，收敛速度慢是主要缺点。为了避免EM算法的局部收敛，通常使用从几个不同的初始值的多次运行。然后，该算法可能需要大量的迭代和长的计算时间来找到最大似然估计。EM算法的计算加速比适用于这些问题。给出了EM算法加速收敛的算法，并将其应用于混合模型估计。数值实验研究的性能的加速算法的迭代次数和计算时间。
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.