# Flexible Clustering for High-Dimensional Data via Mixtures of Joint Generalized Hyperbolic Models

arXiv: https://arxiv.org/abs/1705.03130 · HTML: https://mlsanity.com/paper/1705.03130/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2017.

## Built on by (most cited first, 2 of 2)

- [Finite mixture of skewed sub-Gaussian stable distributions](https://mlsanity.com/paper/2205.14067/) (2022)
- [Parsimonious Mixtures of Skewed Bilinear Factor Analyzers](https://mlsanity.com/paper/2607.14297/) (2026)

## Abstract (arXiv)

A mixture of joint generalized hyperbolic distributions (MJGHD) is introduced for asymmetric clustering for high-dimensional data. The MJGHD approach takes into account the cluster-specific subspace, thereby limiting the number of parameters to estimate while also facilitating visualization of results. Identifiability is discussed, and a multi-cycle ECM algorithm is outlined for parameter estimation. The MJGHD approach is illustrated on two real data sets, where the Bayesian information criterion is used for model selection.