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Department of Statistics, The Ohio State University
Statistics and Biostatistics Colloquium Series
Statistical Learning and Geometry
Mikhail Belkin
Department of Computer Science and Engineering, The Ohio State University
3:30PM - Thursday, October 5, 2006
Room 170, Eighteenth Avenue Bldg. (EA 170)
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.
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