« October 23, 2009
Eric Eide and Rob Ricci |
HOME
| September 25, 2009
Elaine Cohen, Professor & Rich Riesenfeld, Professor
1250 WEB »
October 2, 2009
Ross Whitaker, Professor
Title: Parameterizing high-dimensional data sets with kernel map manifolds
Many important data analysis problems come in in the form of a set of
data points each of which contains a large number of measurements,
which can be considered scattered data in a very high dimensional
space. Visualizing and analyzing such data is challenging, because
the dimensionality of the ambient space makes visualization and
statistical analysis quite difficult. However, often such data sets do
not fill the ambient space, but rather lie close to some lower
dimensional manifold. If the manifold is linear, then principal
component analysis and other linear models can extract the best
fitting models. However, the nonlinear case demands a more
sophisticated set of tools for learning the underlying structure of
high-dimensional data. This talk examines the problem of manifold
learning from a machine learning point of view and describes new tools
that make the connection between manifold learning and the statistical
generalization of PCA, called principal surfaces. We also present
results on examples of visualization and analysis of high-dimensional
data from graphics, perception, and medicine.
Posted by Admin at September 1, 2009 04:11 PM