Geometric methods

Tuesday, October 28, 2008 - 3:55pm - 4:45pm
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.
Monday, October 7, 2013 - 10:15am - 11:05am
Susan Holmes (Stanford University)
Modern Data is currently available as combinations of heterogeneous data, medical images can be combined with genetic sequences and protein network information, the challenges of this heterogeneity involve choosing methods that can integrate multiple tables of data without loss of information, I will talk mostly about methods that use various metrics and operator approaches.
Thursday, September 29, 2011 - 2:00pm - 3:00pm
We discuss several geometric approaches to the study of data sets in high-dimensional spaces that are assumed to have low-intrinsci dimension. On the one hand, we discuss diffusion geometry type of approaches, based on constructing proximity graphs between the data points in high dimensions, and using diffusion processes on such graphs to construct coordinates for the data and perform learning tasks.
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