# Robust mixture modelling using sub-Gaussian stable distribution

arXiv: https://arxiv.org/abs/1701.06749 · HTML: https://mlsanity.com/paper/1701.06749/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2017.

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

- [Adversarially robust clustering with optimality guarantees](https://mlsanity.com/paper/2306.09977/) (2023)

## Abstract (arXiv)

Heavy-tailed distributions are widely used in robust mixture modelling due to possessing thick tails. As a computationally tractable subclass of the stable distributions, sub-Gaussian α-stable distribution received much interest in the literature. Here, we introduce a type of expectation maximization algorithm that estimates parameters of a mixture of sub-Gaussian stable distributions. A comparative study, in the presence of some well-known mixture models, is performed to show the robustness and performance of the mixture of sub-Gaussian α-stable distributions for modelling, simulated, synthetic, and real data.