# Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions

arXiv: https://arxiv.org/abs/1903.05054 · HTML: https://mlsanity.com/paper/1903.05054/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2019.

## Built on by (most cited first, 1 of 1)

- [Clustering Three-Way Data with Outliers](https://mlsanity.com/paper/2310.05288/) (2023)

## Abstract (arXiv)

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.