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Geometry and Statistical Learning
Applied Math| Speaker: | Misha Belkin, Ohio State University |
| Location: | 1147 MSB |
| Start time: | Mon, Oct 23 2006, 4:10PM |
Description
I will discuss why geometry of high-dimensional data may be useful for
various problems of statistical learning. In particular, I will talk about
the role of the Laplace operator on a manifold, explain how it may be
estimated from point-cloud data, when the underlying manifold is not known,
and present some resulting algorithms.
I will also discuss connections to the heat equation and show some
convergence results.