Classification with the matrix-variate-<i>t</i> distribution

Matrix-variate distributions can intuitively model the dependence structure of matrix-valued observations that arise in applications with multivariate time series, spatio-temporal or repeated measures. This paper develops an Expectation-Maximization algorithm for discriminant analysis and classification with matrix-variate <i>t</i>-distributions. The methodology shows promise on simulated datasets or when applied to the forensic matching of fractured surfaces or the classification of functional Magnetic Resonance, satellite or hand gestures images.矩阵变量分布（Matrix-variate distributions）能够直观建模多元时间序列、时空数据或重复测量应用中产生的矩阵值观测的依赖结构。本文提出了一种适配矩阵变量t分布（matrix-variate t-distributions）的判别分析与分类期望最大化（Expectation-Maximization）算法。该方法在模拟数据集上展现出良好的应用前景，同时可应用于断裂表面的司法鉴定匹配、功能磁共振（functional Magnetic Resonance）、卫星或手势图像的分类任务。