Probabilistic graph partitioning

author: David Barber, Centre for Computational Statistics and Machine Learning, University College London
published: Sept. 7, 2007,   recorded: September 2007,   views: 6742
Categories

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 Graph Partitioning for applications in Web Mining and Collaborative Filtering. Our approach is based on predicting the presence/absence of a directed link based on a form of probabilistic mixture model. Being based on a generative model of directed graphs, we are able to apply an approximate Bayesian treatment to automatically select an appropriate number of partitions. We will discuss an application in Collaborative Filtering and comment on relations to mixed membership models, Latent Dirichlet Allocation and Probabilistic Latent Semantic Analysis.

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: