Department of Mathematics,
"Non-Parametric Estimation of Manifolds from Noisy Data"
Nov 29, 2021 Schedule:
- 2:30 to 3:30 PM Eastern Time (US and Canada)
Meeting # 92567655620
A common task in many data-driven applications is to find a low dimensional manifold that describes the data accurately. Estimating a manifold from noisy samples has proven to be a challenging task. Indeed, even after decades of research, there is no (computationally tractable) algorithm that accurately estimates a manifold from noisy samples with a constant level of noise.
In this talk, we will present a method that estimates a manifold and its tangent in the ambient space. Moreover, we establish rigorous convergence rates, which are essentially as good as existing convergence rates for function estimation.
This is a joint work with Barak Sober.