Finite Mixtures of Skewed Matrix Variate Distributions
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Abstract (arXiv)
Clustering is the process of finding underlying group structures in data. Although mixture model-based clustering is firmly established in the multivariate case, there is a relative paucity of work on matrix variate distributions and none for clustering with mixtures of skewed matrix variate distributions. Four finite mixtures of skewed matrix variate distributions are considered. Parameter estimation is carried out using an expectation-conditional maximization algorithm, and both simulated and real data are used for illustration.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Mar 2017.
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Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 1.
2 papers.
- 2023Clustering Three-Way Data with Outlierscited by 0
- 2026Parsimonious Mixtures of Skewed Bilinear Factor Analyzerscited by 0
- 2026Parsimonious Mixtures of Skewed Bilinear Factor Analyzers15 Jul 2026
- 2023Clustering Three-Way Data with Outliers8 Oct 2023