# Clustering Three-Way Data with Outliers

arXiv: https://arxiv.org/abs/2310.05288 · HTML: https://mlsanity.com/paper/2310.05288/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 53 references that are in the map.
- First posted on arXiv 8 Oct 2023.

## Builds on (most cited first, 12 of 12)

- [Parsimonious mixtures of multivariate contaminated normal distributions](https://mlsanity.com/paper/1305.4669/) (2013)
- [Mixtures of Multivariate Power Exponential Distributions](https://mlsanity.com/paper/1506.04137/) (2015)
- [Finite mixtures of matrix-variate Poisson-log normal distributions for three-way count data](https://mlsanity.com/paper/1807.08380/) (2018)
- [Mixtures of Skew-t Factor Analyzers](https://mlsanity.com/paper/1305.4301/) (2013)
- [Asymmetric Clusters and Outliers: Mixtures of Multivariate Contaminated Shifted Asymmetric Laplace Distributions](https://mlsanity.com/paper/1402.6744/) (2014)
- [Finding Outliers in Gaussian Model-Based Clustering](https://mlsanity.com/paper/1907.01136/) (2019)
- [Covariance pattern mixture models for the analysis of multivariate heterogeneous longitudinal data](https://mlsanity.com/paper/1401.1301/) (2014)
- [Finite Mixtures of Skewed Matrix Variate Distributions](https://mlsanity.com/paper/1703.08882/) (2017)
- [Mixtures of Contaminated Matrix Variate Normal Distributions](https://mlsanity.com/paper/2005.03861/) (2020)
- [Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions](https://mlsanity.com/paper/1903.05054/) (2019)
- [Model-based clustering and classification using mixtures of multivariate skewed power exponential distributions](https://mlsanity.com/paper/1907.01938/) (2019)
- [Assessing and Visualizing Matrix Variate Normality](https://mlsanity.com/paper/1910.02859/) (2019)

## Abstract (arXiv)

Matrix-variate distributions are a relatively recent addition to the model-based clustering literature, 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.