标题： 简约的矩阵变量双线性因子分析器混合模型 显示英文标题

标题： Parsimonious Mixtures of Matrix Variate Bilinear Factor Analyzers

摘要： 多年来，数据的维度变得越来越高，这促使对降维技术的需求不断增加。 这可能尤其适用于聚类（无监督分类）以及半监督和监督分类。 文献中已经提出了许多针对两向（多元）数据的方法，而最近也出现了针对三向（矩阵变量）数据的方法。 其中一种方法是矩阵变量双线性因子分析器的混合模型（MMVBFA）。 在此，我们提出了总共64种简约的MMVBFA模型。 模拟数据和实际数据用于说明。 显示英文摘要

摘要： Over the years, data have become increasingly higher dimensional, which has prompted an increased need for dimension reduction techniques. This is perhaps especially true for clustering (unsupervised classification) as well as semi-supervised and supervised classification. Many methods have been proposed in the literature for two-way (multivariate) data and quite recently methods have been presented for three-way (matrix variate) data. One such such method is the mixtures of matrix variate bilinear factor analyzers (MMVBFA) model. Herein, we propose of total of 64 parsimonious MMVBFA models. Simulated and real data are used for illustration.