International audienceWe consider the manifold of positive definite matrices endowed with the Fisher Riemannian metric and some other distances commonly used in information theory. We show that for each of them the best approximant to A from the unitary orbit of another matrix B commutes with A
We introduce a new Riemannian framework for the set of symmetric positive-definite (SPD) matrices, a...
Information geometry studies the dually flat structure of a manifold, highlighted by the generalized...
In this paper, we develop a new classification method for manifold-valued data in the framework of p...
Abstract. There has been considerable work on matrix approximation prob-lems in the space of matrice...
AbstractThe Riemannian metric on the manifold of positive definite matrices is defined by a kernel f...
AbstractOn the manifold of positive definite matrices, a Riemannian metric Kϕ is associated with a p...
In many modern statistical applications the data complexity may require techniques that exploit the ...
We show how the Riemannian distance on n++, the cone of n×n real symmetric or complex Hermitian po...
International audienceOn the space of positive definite matrices we consider distance functions of t...
International audienceOn the space of positive definite matrices we consider distance functions of t...
AbstractA short and simple proof is given for the inequality that shows that positive definite matri...
AbstractThe Riemannian metric on the manifold of positive definite matrices is defined by a kernel f...
International audienceSeveral Riemannian metrics and families of Riemannian metrics were defined on ...
Symmetric Positive Definite (SPD) matrices have become popular to encode image information. Accounti...
The Bures-Wasserstein distance is a Riemannian distance on the space of positive definite Hermitian ...
We introduce a new Riemannian framework for the set of symmetric positive-definite (SPD) matrices, a...
Information geometry studies the dually flat structure of a manifold, highlighted by the generalized...
In this paper, we develop a new classification method for manifold-valued data in the framework of p...
Abstract. There has been considerable work on matrix approximation prob-lems in the space of matrice...
AbstractThe Riemannian metric on the manifold of positive definite matrices is defined by a kernel f...
AbstractOn the manifold of positive definite matrices, a Riemannian metric Kϕ is associated with a p...
In many modern statistical applications the data complexity may require techniques that exploit the ...
We show how the Riemannian distance on n++, the cone of n×n real symmetric or complex Hermitian po...
International audienceOn the space of positive definite matrices we consider distance functions of t...
International audienceOn the space of positive definite matrices we consider distance functions of t...
AbstractA short and simple proof is given for the inequality that shows that positive definite matri...
AbstractThe Riemannian metric on the manifold of positive definite matrices is defined by a kernel f...
International audienceSeveral Riemannian metrics and families of Riemannian metrics were defined on ...
Symmetric Positive Definite (SPD) matrices have become popular to encode image information. Accounti...
The Bures-Wasserstein distance is a Riemannian distance on the space of positive definite Hermitian ...
We introduce a new Riemannian framework for the set of symmetric positive-definite (SPD) matrices, a...
Information geometry studies the dually flat structure of a manifold, highlighted by the generalized...
In this paper, we develop a new classification method for manifold-valued data in the framework of p...
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