International audienceThis paper addresses the generalization of Principal Component Analysis (PCA) to Riemannian manifolds. Current methods like Principal Geodesic Analysis (PGA) and Geodesic PCA (GPCA) minimize the distance to a "Geodesic subspace". This allows to build sequences of nested subspaces which are consistent with a forward component analysis approach. However, these methods cannot easily be adaptedto a backward analysis and lack symmetry in the parametrization of the subspaces. We propose in [10] a new and more general type of family of subspaces in manifolds, barycentric subspaces, which are implicitly defined as the locus of points which are weighted means of k + 1 reference points. Depending on the generalization of the mea...
Principal Component Analysis (PCA) is a widely used technique for reducing dimensionality of multiva...
In this paper a numerical method to compute principal component geodesics for Kendall's planar shape...
We study the extraction of nonlinear data models in high-dimensional spaces with modified self-organ...
International audienceThis paper addresses the generalization of Principal Component Analysis (PCA) ...
International audienceThis paper investigates the generalization of Principal Component Analysis (PC...
In this dissertation we present a generalization of Principal Component Analysis (PCA) to Riemannian...
International audienceGeneralizing Principal Component Analysis (PCA) to man-ifolds is pivotal for m...
In non-Euclidean data spaces represented by manifolds (or more generally stratified spaces), analogs...
International audienceGeometric statistics aim at shifting the classical paradigm for inference from...
A general framework for a novel non-geodesic decomposition of high-dimensional spheres or high-dimen...
AbstractIn this paper a numerical method to compute principal component geodesics for Kendall’s plan...
We propose a new approach to analyze data that naturally lie on manifolds. We focus on a special cla...
In this article, we develop an asymptotic method for testing hypothesis on the set of all linear sub...
Recent years have witnessed an explosion of data across scientific fields enabled by advances in sen...
Molecular dynamics simulations produce huge datasets of temporal sequences of molecules. It is of in...
Principal Component Analysis (PCA) is a widely used technique for reducing dimensionality of multiva...
In this paper a numerical method to compute principal component geodesics for Kendall's planar shape...
We study the extraction of nonlinear data models in high-dimensional spaces with modified self-organ...
International audienceThis paper addresses the generalization of Principal Component Analysis (PCA) ...
International audienceThis paper investigates the generalization of Principal Component Analysis (PC...
In this dissertation we present a generalization of Principal Component Analysis (PCA) to Riemannian...
International audienceGeneralizing Principal Component Analysis (PCA) to man-ifolds is pivotal for m...
In non-Euclidean data spaces represented by manifolds (or more generally stratified spaces), analogs...
International audienceGeometric statistics aim at shifting the classical paradigm for inference from...
A general framework for a novel non-geodesic decomposition of high-dimensional spheres or high-dimen...
AbstractIn this paper a numerical method to compute principal component geodesics for Kendall’s plan...
We propose a new approach to analyze data that naturally lie on manifolds. We focus on a special cla...
In this article, we develop an asymptotic method for testing hypothesis on the set of all linear sub...
Recent years have witnessed an explosion of data across scientific fields enabled by advances in sen...
Molecular dynamics simulations produce huge datasets of temporal sequences of molecules. It is of in...
Principal Component Analysis (PCA) is a widely used technique for reducing dimensionality of multiva...
In this paper a numerical method to compute principal component geodesics for Kendall's planar shape...
We study the extraction of nonlinear data models in high-dimensional spaces with modified self-organ...