在数据建模和分析中,有限混合体模型被广泛地使用着.然而,如何仅仅针对一组来自于某个有限混合体模型的数据选择出分量或聚类的个数则依然是一个非常困难的问题.由于分量个数是混合体模型的规模度量,其选择问题被称为有限混合体的模型选择问题.最近,针对有限混合体模型,特别是高斯混合模型,一种自动模型选择学习机制逐步发展成熟起来.这种新的机制能够在学习参数的过程中自动地完成模型选择,为数据的建模与分析提供了一种新的思路与途径.本文将对于高斯混合模型或一般有限混合体模型的自动模型选择学习算法及其典型应用进行综述与总结.首先,我们综述了基于贝叶斯阴阳机和谐学习原则的自动模型选择学习算法.然后,我们描述了另一种基于熵惩罚的自动模型选择学习算法.最后,我们给出了自动模型选择学习算法的一些典型的应用.%In data modeling and analysis, finite mixture is widely used.However, the selection of number of components in the mixture for a sample data set is still a rather difficult task.In order to overcome it, many criteria have been proposed to determine the best number of components or clusters in the sample data.Since the number of components is just a scale of the mixture model, its selection is usually referred to as model selection.Recently.a novel Automated Model Selection(AMS)learning mechanism for finite mixture modeling, especially for Gaussian mixture modeling, has been developed such that model selection can be made automatically during parameter learning on the sample data, which provide a new perspective for data modeling and analysis.In the current paper, we survey some main results of AMS on Gaussian mixture or general finite mixture.First, we summarize some AMS learning algorithms on Gaussian or finite mixture based on the Bayesian Ying-Yang (BYY)harmony learning principle.We then describe an entropy penalized AMS learning algorithm on Gaussian mixture.Finally, we present some typical practical applications of these AMS learning algorithms.
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