arXiv Machine Learning September 3, 2026 By Katharine M. Clark, Paul D. McNicholas

Clustering Three-Way Data with Outliers

Read the original on arXiv Machine Learning →

The paper introduces a method for clustering matrix-variate normal data that accounts for outliers. It extends the OCLUST algorithm by employing subset log-likelihood distributions and an iterative trimming procedure. This approach enables robust clustering of complex structured data such as images and time series.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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Robust model-based clustering via mixtures of multivariate pseudo-Voigt distributions

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