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

05/20/2020
by   Florent Bouchard, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/21/2020

Bures-Wasserstein Geometry

The Bures-Wasserstein distance is a Riemannian distance on the space of ...
research
11/02/2018

Information Geometry of Sensor Configuration

In problems of parameter estimation from sensor data, the Fisher Informa...
research
12/14/2022

Mechanics of geodesics in Information geometry

In this article we attempt to formulate Riemannian and Randers-Finsler m...
research
09/21/2021

Towards the Classification of Error-Related Potentials using Riemannian Geometry

The error-related potential (ErrP) is an event-related potential (ERP) e...
research
08/15/2023

Riemannian geometry for efficient analysis of protein dynamics data

An increasingly common viewpoint is that protein dynamics data sets resi...
research
04/03/2017

Clustering in Hilbert simplex geometry

Clustering categorical distributions in the probability simplex is a fun...
research
05/17/2018

Fast, asymptotically efficient, recursive estimation in a Riemannian manifold

Stochastic optimisation in Riemannian manifolds, especially the Riemanni...

Please sign up or login with your details

Forgot password? Click here to reset