Spectral Clustering Based on the Graph p-Laplacian

author: Thomas Bühler, Department of Computer Science, Saarland University
published: Aug. 26, 2009,   recorded: June 2009,   views: 711
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 present a generalized version of spectral clustering using the graph p-Laplacian, a nonlinear generalization of the standard graph Laplacian. We show that the second eigenvector of the graph p-Laplacian interpolates between a relaxation of the normalized and the Cheeger cut. Moreover, we prove that in the limit as p ! 1 the cut found by thresholding the second eigenvector of the graph p-Laplacian converges to the optimal Cheeger cut. Furthermore, we provide an efficient numerical scheme to compute the second eigenvector of the graph p- Laplacian. The experiments show that the clustering found by p-spectral clustering is at least as good as normal spectral clustering, but often leads to signifi cantly better results.

See Also:

Download slides icon Download slides: icml09_buhler_scb_01.pdf (1.5 MB)


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 !

Reviews and comments:

Comment1 Thanos, January 8, 2019 at 10:57 a.m.:

There are the many feature for access the all file and folder to look here http://mycomputerwindows10.com and save the function for how do i get my computer in windows 10.

Write your own review or comment:

make sure you have javascript enabled or clear this field: