Non-Parametric Estimation of Manifolds from Noisy Data

Speaker
Yariv Aizenbud, Yale
Date
12/12/2021–12 - 13:30 - 00:00Add to Calendar 2021-12-12 00:00:00 2021-12-12 13:30:00 Non-Parametric Estimation of Manifolds from Noisy Data In many data-driven applications, the data follows some geometric structure, and the goal is to recover this structure. In many cases, the observed data is noisy and the recovery task is even more challenging. A common assumption is that the data lies on a low dimensional manifold. Estimating a manifold from noisy samples has proven to be a challenging task. Indeed, even after decades of research, there was no (computationally tractable) algorithm that accurately estimates a manifold from noisy samples with a constant level of noise. Department Room 201/216 and zoom https://us02web.zoom.us/j/88319852015 אוניברסיטת בר-אילן - Department of Mathematics mathoffice@math.biu.ac.il Asia/Jerusalem public
Place
Department Room 201/216 and zoom https://us02web.zoom.us/j/88319852015
Abstract

In many data-driven applications, the data follows some geometric structure, and the goal is to recover this structure. In many cases, the observed data is noisy and the recovery task is even more challenging. A common assumption is that the data lies on a low dimensional manifold. Estimating a manifold from noisy samples has proven to be a challenging task. Indeed, even after decades of research, there was no (computationally tractable) algorithm that accurately estimates a manifold from noisy samples with a constant level of noise.

Last Updated Date : 12/12/2021