A geometric framework for asymptotic inference of principal subspaces in PCA - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Preprints, Working Papers, ... Year : 2022

A geometric framework for asymptotic inference of principal subspaces in PCA

Abstract

In this article, we develop an asymptotic method for testing hypothesis on the set of all linear subspaces arising from PCA and for constructing confidence regions for this set. This procedure is derived from intrinsic estimation in each Grassmannian, endowed with a structure of Riemannian manifold, to which each of these subspaces belong.
Fichier principal
Vignette du fichier
Geometric_Asymptotic_Inference_Subspace_PCA.pdf (404.58 Ko) Télécharger le fichier

Dates and versions

hal-03842125 , version 1 (07-11-2022)

Identifiers

Cite

Dimbihery Rabenoro, Xavier Pennec. A geometric framework for asymptotic inference of principal subspaces in PCA. 2022. ⟨hal-03842125⟩
22 View
48 Download

Altmetric

Share

Gmail Facebook X LinkedIn More