Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions

2019/03/12 by Sochaniwsky, Alexa A., Michael P. B. Gallaugher, Gallaugher, Michael P. B. +4
Computer Science · Environmental Science · #Bayesian Methods and Mixture Models #Soil Geostatistics and Mapping #Advanced Clustering Algorithms Research

paper · pdf · doi:10.48550/arxiv.1903.05054

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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