Large-Scale Graph-based Transductive Inference

author: Jeff A. Bilmes, Department of Electrical Engineering, University of Washington
published: Jan. 19, 2010,   recorded: December 2009,   views: 3671

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.


We consider the issue of scalability of graph-based semi-supervised learning (SSL) algorithms. In this context, we propose a fast graph node ordering algorithm that improves parallel spatial locality by being cache cognizant. This approach allows for a linear speedup on a shared-memory parallel machine to be achievable, and thus means that graph-based SSL can scale to very large data sets. We use the above algorithm an a multi-threaded implementation to solve a SSL problem on a 120 million node graph in a reasonable amount of time.

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: