2023/07/20 by Andrea Cappozzo, Cappozzo, Andrea, Alessandro Casa +3 · 2 citations
Computer Science · Mathematics · Social Sciences · #Bayesian Methods and Mixture Models #Computation (stat.CO) #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Statistical Methods and Bayesian Inference
paper · pdf · doi:10.48550/arxiv.2307.10673
openalex publication_date 2023/07/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Mixtures of matrix Gaussian distributions provide a probabilistic framework for clustering continuous matrix-variate data, which are becoming increasingly prevalent in various fields. Despite its widespread adoption and successful application, this approach suffers from over-parameterization issues, making it less suitable even for matrix-variate data of moderate size. To overcome this drawback, we introduce a sparse model-based clustering approach for three-way data. Our approach assumes that the matrix mixture parameters are sparse and have different degree of sparsity across clusters, allowing to induce parsimony in a flexible manner. Estimation of the model relies on the maximization of a penalized likelihood, with specifically tailored group and graphical lasso penalties. These penalties enable the selection of the most informative features for clustering three-way data where variables are recorded over multiple occasions and allow to capture cluster-specific association structures. The proposed methodology is tested extensively on synthetic data and its validity is demonstrated in application to time-dependent crime patterns in different US cities.