Riemannian statistics meets random matrix theory: towards learning from high-dimensional covariance matrices

TL;DR

This paper establishes a novel connection between Riemannian Gaussian distributions on high-dimensional covariance matrices and random matrix theory, enabling efficient approximation of normalising factors and advancing applications in high-dimensional statistical modeling.

Contribution

It introduces a new method linking Riemannian Gaussian distributions to random matrix ensembles, providing practical approximations for normalising factors in high dimensions.

Findings

Approximate normalising factors with inverse square error decay.

Efficient computation for high-dimensional covariance matrices.

Extension to block-Toeplitz covariance matrix distributions.

Abstract

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 these applications: there seems to exist no practical method of computing the normalising factors associated with Riemannian Gaussian distributions on spaces of high-dimensional covariance matrices. The present paper shows that this missing method comes from an unexpected new connection with random matrix theory. Its main contribution is to prove that Riemannian Gaussian distributions of real, complex, or quaternion covariance matrices are equivalent to orthogonal, unitary, or symplectic log-normal matrix ensembles. This equivalence yields a highly efficient approximation of the normalising factors, in terms of a rather simple analytic expression. The error due to this approximation decreases like the inverse square of dimension. Numerical experiments are conducted which demonstrate how this new approximation can unlock the difficulties which have impeded applications to real-world datasets of high-dimensional covariance matrices. The paper then turns to Riemannian Gaussian distributions of block-Toeplitz covariance matrices. These are equivalent to yet another kind of random matrix ensembles, here called "acosh-normal" ensembles. Orthogonal and unitary "acosh-normal" ensembles correspond to the cases of block-Toeplitz with Toeplitz blocks, and block-Toeplitz (with general blocks) covariance matrices, respectively.