Clustering Three-Way Data with Outliers
This work addresses a gap in the limited literature on handling outliers in matrix-variate clustering, which is relevant for domains like image and time series analysis, but it is incremental as it builds upon existing methods.
The paper tackles the problem of clustering matrix-variate data, such as images and time series, in the presence of outliers, by extending the OCLUST algorithm to detect and trim outliers using an iterative approach based on subset log-likelihoods.
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.