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Journal of Classification

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

  • 06-01-2024
Published in:
Authors
Paul McLaughlin
Brian C. Franczak
Adam B. Kashlak
Published in
Journal of Classification | Issue 1/2024

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Abstract

The article presents a new family of parsimonious contaminated shifted asymmetric Laplace mixtures (PCSALM) for unsupervised classification, addressing the limitations of Gaussian mixture models (GMMs) in handling asymmetric data and outliers. The PCSALM is derived from a factor analyzer covariance decomposition and incorporates constraints to reduce parameterization, making it suitable for high-dimensional data. The article introduces an alternating expectation-conditional maximization (AECM) algorithm for parameter estimation and demonstrates the PCSALM's superior performance through simulation studies and real data analyses, highlighting its ability to handle both asymmetric clusters and contamination effectively.
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previous article Model-Based Clustering with Nested Gaussian Clusters
next article Nonparametric Cognitive Diagnosis When Attributes Are Polytomous
Title
Unsupervised Classification with a Family of Parsimonious Contaminated Shifted Asymmetric Laplace Mixtures
Authors
Paul McLaughlin
Brian C. Franczak
Adam B. Kashlak
Publication date
06-01-2024
Publisher
Springer US
Published in
Journal of Classification / Issue 1/2024
Print ISSN: 0176-4268
Electronic ISSN: 1432-1343
DOI
https://doi.org/10.1007/s00357-023-09460-0
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