Restricted Riemannian geometry for positive semidefinite matrices

Abstract

We introduce the manifold of {\it restricted} $n\times n$ positive semidefinite matrices of fixed rank $p$, denoted $S(n,p{)}^{\ast }$. The manifold itself is an open and dense submanifold of $S(n,p)$, the manifold of $n\times n$ positive semidefinite matrices of the same rank $p$, when both are viewed as manifolds in ${\mathbb{R}}^{n\times n}$. This density is the key fact that makes the consideration of $S(n,p{)}^{\ast }$ statistically meaningful. We furnish $S(n,p{)}^{\ast }$ with a convenient, and geodesically complete, Riemannian geometry, as well as a Lie group structure, that permits analytical closed forms for endpoint geodesics, parallel transports, Fréchet means, exponential and logarithmic maps. This task is done partly through utilizing a {\it reduced} Cholesky decomposition, whose algorithm is also provided. We produce a second algorithm from this framework to estimate principal eigenspaces and demonstrate its superior performance over other existing algorithms.