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### Non-Parametric Estimation of Manifolds from Noisy Data

**Mathematics of Data & Decisions**

Speaker: | Yariv Aizenbud, Yale |

Location: | |

Start time: | Tue, May 24 2022, 1:10PM |

In many data-driven applications, the data follows some

Estimating a manifold from noisy samples has proven

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.

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.

In this talk, we will present a method that estimates a manifold and its

tangent. Moreover, we establish convergence rates, which are essentially

as good as existing convergence rates for function estimation.