In recent years, data have become increasingly higher dimensional and, therefore, an increased need has arisen for dimension reduction techniques for clustering. Although such techniques are firmly established in the literature for multivariate data, there is a relative paucity in the area of matrix variate, or three-way, data. Furthermore, the few methods that are available all assume matrix variate normality, which is not always sensible if cluster skewness or excess kurtosis is present. Mixtures of bilinear factor analyzers using skewed matrix variate distributions are proposed. In all, four such mixture models are presented, based on matrix variate skew-t, generalized hyperbolic, variance-gamma, and normal inverse Gaussian distributions, respectively.

中文翻译：

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偏矩阵变量双线性因子分析仪的混合物

近年来，数据的维数越来越高，因此，对用于聚类的维数减少技术的需求日益增加。尽管在文献中已针对多变量数据牢固地建立了这样的技术，但是在矩阵变量或三向数据方面相对缺乏。此外，可用的几种方法都假定矩阵变量正态性，如果出现簇偏度或峰度过大，这并不总是明智的。提出了使用偏斜矩阵变量分布的双线性因子分析仪的混合物。总之，四个这样的混合模型被呈现，基于矩阵变量skew-吨，广义双曲线，方差-γ，和正常逆高斯分布，分别。