The aim of the present research work is to investigate algorithms to compute empirical averages of finite sets of sample-points over the Stiefel manifold by extending the notion of Pythagoras' arithmetic averaging over the real line to a curved manifold. The idea underlying the developed algorithms is that sample-points on the Stiefel manifold get mapped onto a tangent space, where the average is taken, and then the average point on the tangent space is brought back to the Stiefel manifold, via appropriate maps. Numerical experimental results are shown and commented on in order to illustrate the numerical behaviour of the proposed procedure. The obtained numerical results confirm that the developed algorithms converge steadily and in a fe...
In many applications, the measurements are stored in matrices through some data transformation. As a...
Several applications in optimization, image, and signal processing deal with data belonging to matri...
Statistical inference for manifolds attracts much attention because of its power of working with mor...
The aim of the present research work is to investigate algorithms to compute empirical averages of f...
The aim of the present contribution is to extend the algorithm introduced in the paper S. Fiori and ...
The present paper elaborates on tangent-bundle maps on the Grassmann manifold, with application to s...
This paper presents a new, provably-convergent algorithm for computing the flag-mean and flag-median...
When the orientation of an object lies in a space of non-zero curvature usual distributions of proba...
The present research work proposes a new fast fixed-point average-value learning algorithm on the co...
The present research takes its moves from previous contributions by the present authors on two topic...
AbstractThis paper develops the theory of density estimation on the Stiefel manifoldVk,m, whereVk,mi...
The present paper aims at introducing a novel procedure for designing an averaging algorithm for a c...
We address the problem of estimating optimal curves for interpolation, smoothing, and prediction of ...
An interest in infinite-dimensional manifolds has recently appeared in Shape Theory. An example is t...
International audienceGeometric statistics aim at shifting the classical paradigm for inference from...
In many applications, the measurements are stored in matrices through some data transformation. As a...
Several applications in optimization, image, and signal processing deal with data belonging to matri...
Statistical inference for manifolds attracts much attention because of its power of working with mor...
The aim of the present research work is to investigate algorithms to compute empirical averages of f...
The aim of the present contribution is to extend the algorithm introduced in the paper S. Fiori and ...
The present paper elaborates on tangent-bundle maps on the Grassmann manifold, with application to s...
This paper presents a new, provably-convergent algorithm for computing the flag-mean and flag-median...
When the orientation of an object lies in a space of non-zero curvature usual distributions of proba...
The present research work proposes a new fast fixed-point average-value learning algorithm on the co...
The present research takes its moves from previous contributions by the present authors on two topic...
AbstractThis paper develops the theory of density estimation on the Stiefel manifoldVk,m, whereVk,mi...
The present paper aims at introducing a novel procedure for designing an averaging algorithm for a c...
We address the problem of estimating optimal curves for interpolation, smoothing, and prediction of ...
An interest in infinite-dimensional manifolds has recently appeared in Shape Theory. An example is t...
International audienceGeometric statistics aim at shifting the classical paradigm for inference from...
In many applications, the measurements are stored in matrices through some data transformation. As a...
Several applications in optimization, image, and signal processing deal with data belonging to matri...
Statistical inference for manifolds attracts much attention because of its power of working with mor...