# Parsimonious Skew Mixture Models for Model-Based Clustering and Classification

arXiv: https://arxiv.org/abs/1302.2373 · HTML: https://mlsanity.com/paper/1302.2373/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2013.

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

- [Finite Mixtures of Multivariate Poisson-Log Normal Factor Analyzers for Clustering Count Data](https://mlsanity.com/paper/2311.07762/) (2023)
- [Mixture models for data with unknown distributions](https://mlsanity.com/paper/2502.19605/) (2025)

## Abstract (arXiv)

In recent work, robust mixture modelling approaches using skewed distributions have been explored to accommodate asymmetric data. We introduce parsimony by developing skew-t and skew-normal analogues of the popular GPCM family that employ an eigenvalue decomposition of a positive-semidefinite matrix. The methods developed in this paper are compared to existing models in both an unsupervised and semi-supervised classification framework. Parameter estimation is carried out using the expectation-maximization algorithm and models are selected using the Bayesian information criterion. The efficacy of these extensions is illustrated on simulated and benchmark clustering data sets.