# Gaussian Parsimonious Clustering Models with Covariates and a Noise Component

arXiv: https://arxiv.org/abs/1711.05632 · HTML: https://mlsanity.com/paper/1711.05632/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2017.

## Built on by (most cited first, 4 of 4)

- [StepMix: A Python Package for Pseudo-Likelihood Estimation of Generalized Mixture Models with External Variables](https://mlsanity.com/paper/2304.03853/) (2023)
- [A similarity-based Bayesian mixture-of-experts model](https://mlsanity.com/paper/2012.02130/) (2020)
- [A self-adaptive and robust fission clustering algorithm via heat diffusion and maximal turning angle](https://mlsanity.com/paper/2102.03794/) (2021)
- [Mixture of partially linear experts](https://mlsanity.com/paper/2405.02905/) (2024)

## Abstract (arXiv)

We consider model-based clustering methods for continuous, correlated data that account for external information available in the presence of mixed-type fixed covariates by proposing the MoEClust suite of models. These models allow different subsets of covariates to influence the component weights and/or component densities by modelling the parameters of the mixture as functions of the covariates. A familiar range of constrained eigen-decomposition parameterisations of the component covariance matrices are also accommodated. This paper thus addresses the equivalent aims of including covariates in Gaussian parsimonious clustering models and incorporating parsimonious covariance structures into all special cases of the Gaussian mixture of experts framework. The MoEClust models demonstrate significant improvement from both perspectives in applications to both univariate and multivariate data sets. Novel extensions to include a uniform noise component for capturing outliers and to address initialisation of the EM algorithm, model selection, and the visualisation of results are also proposed.