October 5, 2006
All Day
209 W. Eighteenth Ave. (EA), Room 170
Title
Statistical Learning and Geometry
Speaker
Mikhail Belkin, The Ohio State University
Abstract
I will discuss why geometry of high-dimensional data may be useful for various inferential problems, including data representation, clustering and semi-supervised learning. In particular, I will talk about the role of the Laplace operator on a manifold, explain how it may be estimated from sampled data, when the underlying manifold is not known, and present some resulting algorithms.
Meet the speaker in Room 212 Cockins Hall at 4:30 p.m. Refreshments will be served.
