__Claremont Graduate University__ | __Claremont McKenna__ | __Harvey Mudd__ | __Pitzer__ | __Pomona__ | __Scripps__

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When

Start: 09/04/2013 - 1:15pm

End : 09/04/2013 - 2:15pm

End : 09/04/2013 - 2:15pm

Category

Applied Math Seminar

Speaker

Guangliang Chen (CMC)

Abstract

Data sets are often modeled as samples from a probability distribution

in high dimensions, but assumed to have some interesting low-dimensional

structure, for example that of a -dimensional submanifold of ,

with . When is simply a linear subspace, one may encode

efficiently the data by projection onto a dictionary consisting of the

top singular vectors, with a cost of (where is the data size).

When is nonlinear, there are no "explicit" and fast constructions of

dictionaries that achieve a similar efficiency: typically one uses

either random dictionaries, or dictionaries obtained by black-box global

optimization. In this talk we present an efficient construction of

data-dependent dictionaries for manifold-type data based on a geometric

multiresolution analysis, aiming at efficiently encoding and

manipulating the data. We will also mention its application to anomaly detection.

Where

CMC Campus, Adams Hall, Davidson (the largest lecture room on the first floor)