论文标题
PCA主要子空间渐近推断的几何框架
A geometric framework for asymptotic inference of principal subspaces in PCA
论文作者
论文摘要
在本文中，我们开发了一种渐近方法，用于构建由PCA引起的所有线性子空间集的置信区域，我们从该集合中得出了该集合的假设检验。我们的方法基于Riemannian歧管的几何形状，其中一些线性子空间被赋予。
In this article, we develop an asymptotic method for constructing confidence regions for the set of all linear subspaces arising from PCA, from which we derive hypothesis tests on this set. Our method is based on the geometry of Riemannian manifolds with which some sets of linear subspaces are endowed.
