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Found 6 papers, 2 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.
Riemannian optimization for non-centered mixture of scaled Gaussian distributions
The proposed Riemannian gradient descent algorithm is leveraged to solve this second minimization problem.
Robust Geometric Metric Learning
This paper proposes new algorithms for the metric learning problem.
Riemannian geometry for Compound Gaussian distributions: application to recursive change detection
A new Riemannian geometry for the Compound Gaussian distribution is proposed.
