Loading Events
  • This event has passed.
Stochastics and Statistics Seminar

Fitting a putative manifold to noisy data

May 25, 2018 @ 11:00 am - 12:00 pm

Hariharan Narayanan (Tata Institute of Fundamental Research, Mumbai)


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.

MIT Statistics + Data Science Center
Massachusetts Institute of Technology
77 Massachusetts Avenue
Cambridge, MA 02139-4307