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 article 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 to the classification of functional magnetic resonance, satellite, or hand gestures images. Supplementary materials for this article are available online.矩阵变量分布（Matrix-variate distributions）可直观建模多元时间序列、时空或重复测量应用中产生的矩阵值观测的依赖结构。本文针对矩阵变量t分布（t-distributions）的判别分析与分类任务，提出了一种期望最大化算法（expectation-maximization algorithm, EM）。该方法在模拟数据集上展现出良好的应用潜力，同时可成功应用于断裂表面的法医匹配、功能磁共振图像、卫星图像或手势图像的分类任务。本文的补充材料可在线获取。

搜集汇总

背景与挑战

背景概述

该数据集围绕矩阵变量t分布的分类应用展开，开发了一种期望最大化算法用于判别分析，适用于多变量时间序列、时空或重复测量场景。数据集包括代码、数据和补充文档，展示了在模拟数据、法医断裂匹配、功能磁共振成像和图像分类中的实际应用潜力。