UW-PIMS Mathematics Colloquium: Hariharan Narayanan

  • Date: 03/29/2011
  • Time: 15:30
Hariharan Narayanan

University of Washington


Testing the Manifold Hypothesis


Increasingly, we are confronted with very high dimensional data sets. As a
result, methods of avoiding the curse of dimensionality have come to the
forefront of machine learning research. One approach, which relies on
exploiting the geometry of the data, has evolved into a subfield called
manifold learning.

The underlying hypothesis of this field is that due to
constraints that limit the degrees of freedom of the generative process,
data tend to lie near a low dimensional submanifold. This has been
empirically observed to be the case, for example, in speech and video data.
Although there are many widely used algorithms motivated by this hypothesis,
the basic question of testing this hypothesis is poorly understood. We will
describe an approach to test this hypothesis from random data.

Other Information: 

Location: Raitt Hall, Room 121


For more information please visit University of Washington Department of Mathematics