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Clustering Three-Way Data with Outliers. (arXiv:2310.05288v2 [stat.ML] UPDATED)

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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.

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