We introduce the manifold of {\it restricted} $n\times n$ positive semidefinite matrices of fixed rank $p$, denoted $S(n,p)^{*}$. 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 $S(n,p)^{*}$ statistically meaningful. We furnish $S(n,p)^{*}$ 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, Fr\'echet means, exponential and logarithmic maps. This task is done partly through utilizing a {\it re...
The paper addresses the problem of learning a regression model parameterized by a fixed-rank positiv...
We present a geometric optimization approach to approximate solutions of ma- trix equations by low-r...
We give simple formulas for the canonical metric, gradient, Lie derivative, Riemannian connection, p...
We present a homogeneous space geometry for the manifold of symmetric positive semidefinite matrices...
We present a homogeneous space geometry for the manifold of symmetric positive semidefinite matrices...
We present a homogeneous space geometry for the manifold of symmetric positive semidefinite matrices...
This paper deals with the Riemannian geometry of the set of symmetric positive semidefinite matrices...
This paper introduces a new metric and mean on the set of positive semidefinite matrices of fixed-ra...
This paper explores the well-known identification of the manifold of rank p positivesemidefinitematric...
Abstract. This paper introduces a new metric and mean on the set of positive semidefinite matrices o...
We consider the manifold of rank-p positive-semidefinite matrices of size n, seen as a quotient of t...
The set of covariance matrices equipped with the Bures-Wasserstein distance is the orbit space of th...
The paper addresses the problem of learning a regression model parameterized by a fixed-rank positiv...
AbstractA riemannian metric is introduced in the infinite dimensional manifold Σn of positive operat...
The paper addresses the problem of learning a regression model parameterized by a fixed-rank positiv...
The paper addresses the problem of learning a regression model parameterized by a fixed-rank positiv...
We present a geometric optimization approach to approximate solutions of ma- trix equations by low-r...
We give simple formulas for the canonical metric, gradient, Lie derivative, Riemannian connection, p...
We present a homogeneous space geometry for the manifold of symmetric positive semidefinite matrices...
We present a homogeneous space geometry for the manifold of symmetric positive semidefinite matrices...
We present a homogeneous space geometry for the manifold of symmetric positive semidefinite matrices...
This paper deals with the Riemannian geometry of the set of symmetric positive semidefinite matrices...
This paper introduces a new metric and mean on the set of positive semidefinite matrices of fixed-ra...
This paper explores the well-known identification of the manifold of rank p positivesemidefinitematric...
Abstract. This paper introduces a new metric and mean on the set of positive semidefinite matrices o...
We consider the manifold of rank-p positive-semidefinite matrices of size n, seen as a quotient of t...
The set of covariance matrices equipped with the Bures-Wasserstein distance is the orbit space of th...
The paper addresses the problem of learning a regression model parameterized by a fixed-rank positiv...
AbstractA riemannian metric is introduced in the infinite dimensional manifold Σn of positive operat...
The paper addresses the problem of learning a regression model parameterized by a fixed-rank positiv...
The paper addresses the problem of learning a regression model parameterized by a fixed-rank positiv...
We present a geometric optimization approach to approximate solutions of ma- trix equations by low-r...
We give simple formulas for the canonical metric, gradient, Lie derivative, Riemannian connection, p...