This paper introduces a new metric and mean on the set of positive semidefinite matrices of fixed-rank. The proposed metric is derived from a well-chosen Riemannian quotient geometry that generalizes the reductive geometry of the positive cone and the associated natural metric. The resulting Riemannian space has strong geometrical properties: it is geodesically complete, and the metric is invariant with respect to all transformations that preserve angles (orthogonal transformations, scalings, and pseudoinversion). A meaningful approximation of the associated Riemannian distance is proposed, that can be efficiently numerically computed via a simple algorithm based on SVD. The induced mean preserves the rank, possesses the most desirable char...
AbstractIn this paper we provide a new class of (metric) geometric means of positive definite matric...
We introduce the manifold of {\it restricted} $n\times n$ positive semidefinite matrices of fixed ra...
The paper addresses the problem of learning a regression model parameterized by a fixed-rank positiv...
Abstract. This paper introduces a new metric and mean on the set of positive semidefinite matrices o...
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...
The generalization of the geometric mean of positive scalars to positive definite matrices has attra...
AbstractOn the manifold of positive definite matrices, a Riemannian metric Kϕ is associated with a p...
This paper deals with the Riemannian geometry of the set of symmetric positive semidefinite matrices...
AbstractThe Riemannian metric on the manifold of positive definite matrices is defined by a kernel f...
This paper explores the well-known identification of the manifold of rank p positivesemidefinitematric...
The geometric mean of two positive definite matrices has been defined in several ways and studied by...
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...
AbstractIn this paper we provide a new class of (metric) geometric means of positive definite matric...
We introduce the manifold of {\it restricted} $n\times n$ positive semidefinite matrices of fixed ra...
The paper addresses the problem of learning a regression model parameterized by a fixed-rank positiv...
Abstract. This paper introduces a new metric and mean on the set of positive semidefinite matrices o...
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...
The generalization of the geometric mean of positive scalars to positive definite matrices has attra...
AbstractOn the manifold of positive definite matrices, a Riemannian metric Kϕ is associated with a p...
This paper deals with the Riemannian geometry of the set of symmetric positive semidefinite matrices...
AbstractThe Riemannian metric on the manifold of positive definite matrices is defined by a kernel f...
This paper explores the well-known identification of the manifold of rank p positivesemidefinitematric...
The geometric mean of two positive definite matrices has been defined in several ways and studied by...
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...
AbstractIn this paper we provide a new class of (metric) geometric means of positive definite matric...
We introduce the manifold of {\it restricted} $n\times n$ positive semidefinite matrices of fixed ra...
The paper addresses the problem of learning a regression model parameterized by a fixed-rank positiv...