Matrix-variate distributions are a recent addition to the model-based clustering field, thereby making it possible to analyze data in matrix form with complex structure such as images and time series. Due to its recent appearance, there is limited literature on matrix-variate data, with even less on dealing with outliers in these models. An approach for clustering matrix-variate normal data with outliers is discussed. The approach, which uses the distribution of subset log-likelihoods, extends the OCLUST algorithm to matrix-variate normal data and uses an iterative approach to detect and trim outliers.

翻译：矩阵变量分布是模型聚类领域的最新进展，使得能够分析图像和时间序列等具有复杂结构的矩阵形式数据。由于该领域发展较晚，针对矩阵变量数据的文献较为有限，涉及此类模型中异常值处理的文献则更少。本文探讨了一种对含异常值的矩阵变量正态数据进行聚类的方法。该方法利用子集对数似然分布，将OCLUST算法扩展至矩阵变量正态数据，并通过迭代方式检测并剔除异常值。