Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions

Alexa A. Sochaniwsky; Michael P. B. Gallaugher; Yang Tang; Paul D. McNicholas

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arXiv · 1903.05054

Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions

Alexa A. Sochaniwsky·Michael P. B. Gallaugher·Yang Tang·Paul D. McNicholas

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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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Alexa A. Sochaniwsky, Michael P. B. Gallaugher, Yang Tang, Paul D. McNicholas. 2019-03-12. Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions. https://arxiv.org/abs/1903.05054

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