Clustering Three-Way Data with Outliers
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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.
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References · 12
- 2013Parsimonious mixtures of multivariate contaminated normal distributionscited by 8
- 2015Mixtures of Multivariate Power Exponential Distributionscited by 6
- 2018Finite mixtures of matrix-variate Poisson-log normal distributions for three-way count datacited by 6
- 2013Mixtures of Skew-t Factor Analyzerscited by 3
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- 2019Finding Outliers in Gaussian Model-Based Clusteringcited by 3
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- 2019Model-based clustering and classification using mixtures of multivariate skewed power exponential distributionscited by 1
- 2019Assessing and Visualizing Matrix Variate Normalitycited by 1
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