Denoising and Interior Detection Problems

Abstract

Let $\mathcal{M}$ be a compact manifold of ${\mathbb{R}}^d$. The goal of this paper is to decide, based on a sample of points, whether the interior of $\mathcal{M}$ is empty or not. We divide this work in two main parts. Firstly, under a dependent sample which may or may not contain some noise within, we characterize asymptotic properties of an interior detection test based on a suitable control of the dependence. Afterwards, we drop the dependence and consider a model where the points sampled from the manifold are mixed with some points sampled from a different measure (noisy observations). We study the behaviour with respect to the amount of noisy observations, introducing a methodology to identify true manifold points, characterizing convergence properties.

References in corpus (1)

• Statistical Methods in Topological Data Analysis for Complex, High-Dimensional Data