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Fitting a putative manifold to noisy data

May 25 @ 11:00 am - 12:00 pm

Hariharan Narayanan (Tata Institute of Fundamental Research, Mumbai)

E18-304

Abstract: We give a solution to the following question from manifold learning.
Suppose data belonging to a high dimensional Euclidean space is drawn independently, identically distributed from a measure supported on a low dimensional twice differentiable embedded compact manifold M, and is corrupted by a small amount of i.i.d gaussian noise. How can we produce a manifold M whose Hausdorff distance to M is small and whose reach (normal injectivity radius) is not much smaller than the reach of M?
This is joint work with Charles Fefferman, Sergei Ivanov, Yaroslav Kurylev, and Matti Lassas.

Details

Date:
May 25
Time:
11:00 am - 12:00 pm
Event Category:

Venue

E18-304
50 Ames Street
Cambridge, MA 02139

Other

Speaker Name(s)
Hariharan Narayanan (Tata Institute of Fundamental Research, Mumbai)