Finite mixture models are widely used for cluster analysis, including clustering matrix data. Nowadays, high‐dimensional matrix observations arise in a variety of fields. It is known that irrelevant variables can severely affect the performance of clustering procedures. Therefore, it is important to develop algorithms capable of excluding irrelevant variables and focusing on informative attributes in order to achieve good clustering results. Several variable selection approaches have been proposed in the multivariate framework. We introduce and study a variable selection procedure that can be applied in the matrix‐variate context. The methodological developments are supported by several simulation studies and application to real‐life data sets, with good results.

中文翻译：

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关于矩阵混合建模中的变量选择

有限混合模型广泛用于聚类分析，包括聚类矩阵数据。如今，高维矩阵观测值出现在各个领域。众所周知，不相关的变量会严重影响聚类过程的性能。因此，开发能够排除无关变量并关注信息属性的算法以实现良好的聚类结果非常重要。在多变量框架中已经提出了几种变量选择方法。我们介绍并研究了可以在矩阵变量上下文中应用的变量选择程序。该方法学的发展得到了一些模拟研究的支持，并应用于现实生活的数据集，取得了良好的效果。