Riemannian Statistics Meets Random Matrix Theory: Toward Learning From High-Dimensional Covariance Matrices
Riemannian Statistics Meets Random Matrix Theory: Toward Learning From High-Dimensional Covariance Matrices
Riemannian Gaussian distributions were initially introduced as basic building blocks for learning models which aim to capture the intrinsic structure of statistical populations of positive-definite matrices (here called covariance matrices). While the potential applications of such models have attracted significant attention, a major obstacle still stands in the way of …