Testing the Manifold Hypothesis

Friday, March 20, 2015 - 11:20am - 12:00pm
Klaus 1116
Hariharan Narayanan (University of Washington)
The hypothesis that high dimensional data tend to lie in the vicinity of a low dimensional manifold is the basis of manifold learning. The goal of this talk is to outline an algorithm (with accompanying complexity guarantees) for fitting a manifold to an unknown probability distribution supported in a separable Hilbert space, only using i.i.d samples from that distribution.