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首页> 外文期刊>Neural Networks, IEEE Transactions on >Count Data Modeling and Classification Using Finite Mixtures of Distributions
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Count Data Modeling and Classification Using Finite Mixtures of Distributions

机译：使用有限混合分布进行计数数据建模和分类

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摘要

In this paper, we consider the problem of constructing accurate and flexible statistical representations for count data, which we often confront in many areas such as data mining, computer vision, and information retrieval. In particular, we analyze and compare several generative approaches widely used for count data clustering, namely multinomial, multinomial Dirichlet, and multinomial generalized Dirichlet mixture models. Moreover, we propose a clustering approach via a mixture model based on a composition of the Liouville family of distributions, from which we select the Beta-Liouville distribution, and the multinomial. The novel proposed model, which we call multinomial Beta-Liouville mixture, is optimized by deterministic annealing expectation-maximization and minimum description length, and strives to achieve a high accuracy of count data clustering and model selection. An important feature of the multinomial Beta-Liouville mixture is that it has fewer parameters than the recently proposed multinomial generalized Dirichlet mixture. The performance evaluation is conducted through a set of extensive empirical experiments, which concern text and image texture modeling and classification and shape modeling, and highlights the merits of the proposed models and approaches.
机译：在本文中，我们考虑了为计数数据构造准确而灵活的统计表示形式的问题，这在许多领域（例如数据挖掘，计算机视觉和信息检索）中经常遇到。特别是，我们分析并比较了广泛用于计数数据聚类的几种生成方法，即多项式，多项式Dirichlet和多项式广义Dirichlet混合模型。此外，我们基于Liouville分布族的组成，通过混合模型提出了一种聚类方法，从中我们选择Beta-Liouville分布以及多项式。我们通过确定性退火期望最大化和最小描述长度对新提出的模型（称为多项式Beta-Liouville混合物）进行了优化，力求实现计数数据聚类和模型选择的高精度。多项式Beta-Liouville混合的一个重要特征是，与最近提出的多项式广义Dirichlet混合相比，它具有较少的参数。通过一系列广泛的经验实验进行性能评估，这些实验涉及文本和图像纹理建模以及分类和形状建模，并突出了所提出模型和方法的优点。

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