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On EM Estimation for Mixture of Multivariate t-Distributions

机译:多元t分布混合的EM估计

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摘要

This paper formulates a novel expectation maximization (EM) algorithm for the mixture of multivariate t-distributions. By introducing a new kind of "missing" data, we show that the empirically improved iterative algorithm, in literature, for the mixture of multivariate t-distributions is in fact a type of EM algorithm; thus a theoretical analysis is established, which guarantees the empirical algorithm converges to the maximization likelihood estimates of the mixture parameters. Simulated experiment and real experiments on classification and image segmentation confirm the effectiveness of the improved EM algorithm.
机译:本文针对多元t分布的混合提出了一种新颖的期望最大化(EM)算法。通过引入一种新型的“缺失”数据,我们证明了在文献中针对多元t分布混合的经验改进的迭代算法实际上是一种EM算法。因此建立了理论分析,保证了经验算法收敛于混合参数的最大似然估计。在分类和图像分割方面的仿真实验和实际实验证实了改进的EM算法的有效性。

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