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Applied Math Talk: Stochastic similarity matrices and data clustering given by Prof. Denis Gaidashev (Uppsala University)

November 11 @ 4:15 pm - 5:15 pm

Clustering in image analysis is a central technique that allows to classify elements of an image. We describe a simple clustering technique that uses the method of similarity matrices, and an algorithm in which a collection of image elements is treated as a dynamical system. Efficient clustering in this framework   is achieved if the dynamical system admits a spectral gap.

We expand upon recent results in spectral analysis for Gaussian mixture distributions, and in particular, provide conditions for the existence of a spectral gap between the leading and remaining eigenvalues for matrices with entries from a Gaussian mixture with two real univariate components.

Details

Date:
November 11
Time:
4:15 pm - 5:15 pm
Event Category:

Venue

Emmy Noether Room, Millikan 1021, Pomona College
610 N. College Ave.
Claremont, California 91711
+ Google Map

Other

Speaker Name
Denis Gaidashev

Details

Date:
November 11
Time:
4:15 pm - 5:15 pm
Event Category:

Venue

Emmy Noether Room, Millikan 1021, Pomona College
610 N. College Ave.
Claremont, California 91711
+ Google Map

Other

Speaker Name
Denis Gaidashev