# Riemannian geometry for Compound Gaussian distributions: application to recursive change detection

arXiv: https://arxiv.org/abs/2005.10087 · HTML: https://mlsanity.com/paper/2005.10087/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2020.

## Built on by (most cited first, 3 of 3)

- [The Fisher-Rao geometry of CES distributions](https://mlsanity.com/paper/2310.01032/) (2023)
- [Riemannian optimization for non-centered mixture of scaled Gaussian distributions](https://mlsanity.com/paper/2209.03315/) (2022)
- [Leveraging Low-rank Factorizations of Conditional Correlation Matrices in Graph Learning](https://mlsanity.com/paper/2506.10628/) (2025)

## Abstract (arXiv)

A new Riemannian geometry for the Compound Gaussian distribution is proposed. In particular, the Fisher information metric is obtained, along with corresponding geodesics and distance function. This new geometry is applied on a change detection problem on Multivariate Image Times Series: a recursive approach based on Riemannian optimization is developed. As shown on simulated data, it allows to reach optimal performance while being computationally more efficient.