# A robust model-based clustering based on the geometric median and the Median Covariation Matrix

arXiv: https://arxiv.org/abs/2211.08131 · HTML: https://mlsanity.com/paper/2211.08131/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2022.

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

- [Universal Lower Bounds and Optimal Rates: Achieving Minimax Clustering Error in Sub-Exponential Mixture Models](https://mlsanity.com/paper/2402.15432/) (2024)
- [Online and Offline Robust Multivariate Linear Regression](https://mlsanity.com/paper/2404.19496/) (2024)

## Abstract (arXiv)

Grouping observations into homogeneous groups is a recurrent task in statistical data analysis. We consider Gaussian Mixture Models, which are the most famous parametric model-based clustering method. We propose a new robust approach for model-based clustering, which consists in a modification of the EM algorithm (more specifically, the M-step) by replacing the estimates of the mean and the variance by robust versions based on the median and the median covariation matrix. All the proposed methods are available in the R package RGMM accessible on CRAN.