Spectral and geometric methods in learning

Tuesday, October 28, 2008 - 3:55pm - 4:45pm
EE/CS 3-180
Misha Belkin (The Ohio State University)
In recent years a variety of spectral and geometry-based methods have become popular for various tasks of machine learning,
such as dimensionality reduction, clustering and semi-supervised learning. These methods use a model of data as a probability distribution on a manifold, or,
more generally a mixture of manifolds. In the talk I will discuss some of these methods and recent theoretical results on their convergence.
I will also talk about how spectral methods can be used to learn mixtures of Gaussian distributions, which may be considered the simplest case
of multi-manifold data modeling.
MSC Code: