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A novel mixture model using the multivariate normal mean-variance mixture of Birnbaum-Saunders distributions and its application to extrasolar planets

机译:一种新的混合物模型,使用Birnbaum-Saunders分布的多元正常平均值混合物及其在额外行星中的应用

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摘要

This paper presents a new finite mixture model based on the multivariate normal mean variance mixture of Birnbaum-Saunders (NMVBS) distribution. We develop a computationally analytical EM algorithm for model fitting. Due to the dependence of this algorithm on initial values and the number of mixing components, a learning-based EM algorithm and an extended variant are proposed. Numerical simulations show that the proposed algorithms allow for better clustering performance and classification accuracy than some competing approaches. The effectiveness and prominence of the proposed methodology are also shown through an application to an extrasolar planet dataset. (C) 2018 Elsevier Inc. All rights reserved.
机译:本文介绍了一种基于Birnbaum-Saunders(NMVBS)分布的多变量正常平均差异混合物的新型有限混合物模型。 我们开发了一种用于模型配件的计算分析EM算法。 由于该算法对初始值的依赖性和混合组件的数量,提出了一种基于学习的EM算法和扩展变体。 数值模拟表明,所提出的算法允许比某种竞争方法更好地聚类性能和分类精度。 还通过应用于额外行星数据集来显示所提出的方法的有效性和突出。 (c)2018年Elsevier Inc.保留所有权利。

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