Pattern Analysis over Graphs, and Bioinformatics Applications

author: Jean-Philippe Vert, MINES ParisTech
published: Dec. 3, 2009,   recorded: October 2009,   views: 5247
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

 Watch videos:   (click on thumbnail to launch)

Watch Part 1
Part 1 57:50
!NOW PLAYING
Watch Part 2
Part 2 1:14:36
!NOW PLAYING
Watch Part 3
Part 3 42:16
!NOW PLAYING

Description

1. Classification and regression over graphs. Overview: positive definite graph kernels based on walk, subtrees etc.., as well as other non p.d. similarity functions (eg from graph matching) that can be used to compare graphs and do classification/regression with kernel methods. Applications: QSAR in chemistry, image classification

2. Detecting patterns in the context of regression or classification with a graph as prior knowledge over the features. Overview: in a classical regression/classification problem over high-dimensional vectors. Control the complexity, by using priors that can be derived from the graph over the vectors, and how they can be used as penalty functions for classification and regression. This will cover diffusion kernels and other kernels over graphs, fused lasso, structured group lasso. Application in bioinformatics.

See Also:

Download slides icon Download slides: aop09_vert_paog.pdf (5.5┬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: