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Journal of Classification
Erschienen in:

06.01.2024

Unsupervised Classification with a Family of Parsimonious Contaminated Shifted Asymmetric Laplace Mixtures

verfasst von: Paul McLaughlin, Brian C. Franczak, Adam B. Kashlak

Erschienen in: Journal of Classification | Ausgabe 1/2024

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Abstract

A family of parsimonious contaminated shifted asymmetric Laplace mixtures is developed for unsupervised classification of asymmetric clusters in the presence of outliers and noise. A series of constraints are applied to a modified factor analyzer structure of the component scale matrices, yielding a family of twelve models. Application of the modified factor analyzer structure and these parsimonious constraints makes these models effective for the analysis of high-dimensional data by reducing the number of free parameters that need to be estimated. A variant of the expectation-maximization algorithm is developed for parameter estimation with convergence issues being discussed and addressed. Popular model selection criteria like the Bayesian information criterion and the integrated complete likelihood (ICL) are utilized, and a novel modification to the ICL is also considered. Through a series of simulation studies and real data analyses, that includes comparisons to well-established methods, we demonstrate the improvements in classification performance found using the proposed family of models.
Vorheriger Artikel Model-Based Clustering with Nested Gaussian Clusters
Nächster Artikel Nonparametric Cognitive Diagnosis When Attributes Are Polytomous

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Metadaten
Titel
Unsupervised Classification with a Family of Parsimonious Contaminated Shifted Asymmetric Laplace Mixtures
verfasst von
Paul McLaughlin
Brian C. Franczak
Adam B. Kashlak
Publikationsdatum
06.01.2024
Verlag
Springer US
Erschienen in
Journal of Classification / Ausgabe 1/2024
Print ISSN: 0176-4268
Elektronische ISSN: 1432-1343
DOI
https://doi.org/10.1007/s00357-023-09460-0

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