Abstract for April 25, 2006
Some open problems in Dimension Reduction, Inverse Scattering and
Nowdays, we are constantly flooded with information of all sorts
and forms and a common denominator of data analysis in many emerging
fields of current interest are large amounts of observations that have
high dimensionality. In this talk, we will outline work in progress that
relates to the idea of local dimension reduction
in imaging and distributed power networks. In particular, we will discuss
joint work on Paley Weiner theorems in inverse scattering, learning
on curved manifolds and terrain manifold estimation from localization
graphs of sensor and neural networks.
This is joint and ongoing work with T. Devaney (Northeastern), R. Luke
Grabner (Graz), M. Werman (Hebrew U), D.
Wunsch (Missouri-Rolla) and Armit Argawal (Singapore).
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