We present a parsimonious dual-subspace clustering approach for a mixture of matrix-normal distributions. By assuming certain principal components of the row and column covariance matrices are equally important, we express the model in fewer parameters without sacrificing discriminatory information. We derive update rules for an ECM algorithm and set forth necessary conditions to ensure identifiability. We use simulation to demonstrate parameter recovery, and we illustrate the parsimony and competitive performance of the model through two data analyses.

中文翻译：

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矩阵正态分布的对偶子空间简约混合

我们提出了一种用于混合矩阵正态分布的简约双子空间聚类方法。通过假设行协方差矩阵和列协方差矩阵的某些主成分同等重要，我们可以在不牺牲判别信息的情况下用更少的参数来表达模型。我们推导了 ECM 算法的更新规则，并提出了确保可识别性的必要条件。我们使用模拟来演示参数恢复，并通过两个数据分析说明模型的简约性和竞争性能。