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Found 5 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.
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.
Block-wise Minimization-Majorization algorithm for Huber's criterion: sparse learning and applications
Huber's criterion can be used for robust joint estimation of regression and scale parameters in the linear model.
Riemannian geometry for Compound Gaussian distributions: application to recursive change detection
A new Riemannian geometry for the Compound Gaussian distribution is proposed.
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