Riemannian Framework for Robust Covariance Matrix Estimation in Spiked Models

Abstract

This paper aims at providing an original Riemannian geometry to derive robust covariance matrix estimators in spiked models (i.e. when the covariance matrix has a low-rank plus identity structure). The considered geometry is the one induced by the product of the Stiefel manifold and the manifold of Hermitian positive definite matrices, quotiented by the unitary group. One of the main contributions is to consider a Riemannian metric related to the Fisher information metric of elliptical distributions, leading to new representations for the tangent spaces and a new retraction. A new robust covariance matrix estimator is then obtained as the minimizer of Tyler's cost function, redefined directly on the set of low-rank plus identity matrices, and computed with the aforementioned tools. The main interest of this approach is that it appears well suited to the cases where the sample size is lower than the dimension, as illustrated by numerical experiments.

Randomized Time Riemannian Manifold Hamiltonian Monte Carlo

“…One such method which has been proposed assumes the structure of a low rank matrix plus a sparse matrix (see Ross (2013) and Lam (2020)). This structure is known as a spiked covariance structure and has been studied in Bouchard et al (2020), Lam (2020) and Cai et al (2015). There have been interesting applications to finance (Fan et al (2008)), chemometrics (Kritchman and Nadler (2008)), and astronomy (Joachimi (2017)).…”