Spectral methods for analyzing large data sets using reweighted topic modeling

Start: 11/13/2013 - 4:15pm
End  : 11/13/2013 - 5:15pm


Blake Hunter, University of California at Los Angeles


There has been increasing demand to understand the data around us. The flood of social media requires new mathematics, methodologies and procedures to extract knowledge from massive datasets. Spectral methods are numerical linear algebra graph based techniques that use eigenfunctions of a graph to extract the underlying global structure of a dataset. The construction of these, application dependent, graphs require new mathematical ideas that extend data represen- tation, distance, topic modeling and sparsity. The product is often massive matrices that push the limits of matrix computation. This talk looks at content based search with applications to analyzing documents, Twitter microblogs, images and hyperspectral images.

Davidson Lecture Hall, Claremont McKenna College

Hunter.pdf104.5 KB

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