Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions

• URL: http://arxiv.org/abs/1903.05054v2
• Date: Thu, 6 Jun 2024 12:30:18 GMT
• Title: Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions
• Authors: Alexa A. Sochaniwsky, Michael P. B. Gallaugher, Yang Tang, Paul D. McNicholas,
• Abstract summary: Traditional approaches to model-based clustering often fail for high dimensional data. A parametrization of the component scale matrices for the mixture of generalized hyperbolic distributions is proposed. An analytically feasible expectation-maximization algorithm is developed.
• Score: 6.839746711757701
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robust clustering of high-dimensional data is an important topic because clusters in real datasets are often heavy-tailed and/or asymmetric. Traditional approaches to model-based clustering often fail for high dimensional data, e.g., due to the number of free covariance parameters. A parametrization of the component scale matrices for the mixture of generalized hyperbolic distributions is proposed. This parameterization includes a penalty term in the likelihood. An analytically feasible expectation-maximization algorithm is developed by placing a gamma-lasso penalty constraining the concentration matrix. The proposed methodology is investigated through simulation studies and illustrated using two real datasets.

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