Finding Outliers in Gaussian Model-Based Clustering

arXiv:1907.01136v5 Announce Type: replace-cross
Abstract: Clustering, or unsupervised classification, is a task often plagued by outliers. Yet there is a paucity of work on handling outliers in clustering. Outlier identification algorithms tend to fall into three broad categories: outlier inclusion, outlier trimming, and \textit{post hoc} outlier identification methods, with the former two often requiring pre-specification of the number of outliers. The fact that sample Mahalanobis distance is beta-distributed is used to derive an approximate distribution for the log-likelihoods of subset …

abstract algorithms arxiv classification clustering identification inclusion outlier outliers stat.me stat.ml type unsupervised work