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Found 7 papers, 1 papers with code
Random matrix theory improved Fréchet mean of symmetric positive definite matrices
In this study, we consider the realm of covariance matrices in machine learning, particularly focusing on computing Fr\'echet means on the manifold of symmetric positive definite matrices, commonly referred to as Karcher or geometric means.
Intrinsic Bayesian Cramér-Rao Bound with an Application to Covariance Matrix Estimation
In this setup, the chosen Riemannian metric induces a geometry for the parameter manifold, as well as an intrinsic notion of the estimation error measure.
The Fisher-Rao geometry of CES distributions
When dealing with a parametric statistical model, a Riemannian manifold can naturally appear by endowing the parameter space with the Fisher information metric.
Learning Graphical Factor Models with Riemannian Optimization
Graphical models and factor analysis are well-established tools in multivariate statistics.
Riemannian classification of EEG signals with missing values
This paper proposes a strategy to handle missing data for the classification of electroencephalograms using covariance matrices.
Riemannian geometry for Compound Gaussian distributions: application to recursive change detection
A new Riemannian geometry for the Compound Gaussian distribution is proposed.
Random Matrix Improved Covariance Estimation for a Large Class of Metrics
Relying on recent advances in statistical estimation of covariance distances based on random matrix theory, this article proposes an improved covariance and precision matrix estimation for a wide family of metrics.
