Machine Learning Seminar: Estimating a manifold from noisy samples

Nov 19

Monday, November 19, 2018

4:00 pm - 5:00 pm
Gross Hall, Ahmadieh Family Grand Hall, Room 330

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Presenter

Yariv Aizenbud, Tel Aviv University

Estimating a manifold from (possibly noisy) samples appears to be a difficult problem. Indeed, even after decades of research, all manifold learning methods do not actually "learn" the manifold, but rather try to embed it into a low-dimensional Euclidean space. This process inevitably introduces distortions and cannot guarantee a robust estimate of the manifold. In this talk, we will discuss a new method to estimate a manifold in ambient space, which is efficient even in the case of an ambient space of high dimension. The method gives a robust estimate to the manifold and its tangent, without introducing distortions. Moreover, we will show statistical convergence guarantees.

Contact

Dawn, Ariel
919-684-9312
ariel.dawn@duke.edu