Semidefinite ranking on graphs

author: Shankar Vembu, Department of Computer Science, University of Illinois at Urbana-Champaign
published: Sept. 7, 2007,   recorded: September 2007,   views: 418
Categories

Slides

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

We consider the problem of ranking the vertices of an undirected graph given some preference relation. This ranking on graphs problem has been tackled before using spectral relaxations in [1]. Their approach is strongly related to the spectral relaxation made in spectral clustering algorithms. One problem with spectral relaxations that has been found in clustering is that even on simple toy graphs the spectral solution can be arbitrarily far from the optimal one [2]. It has recently been shown that semidefinite relaxations offer in many cases better solutions than spectral ones for clustering [3] and transductive classification [4]. We therefore investigate semidefinite relaxations of ranking on graphs.

See Also:

Download slides icon Download slides: sicgt07_vembu_srog.ppt (1.4┬áMB)


Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: