Generalization theory of two-part code MDL estimator

author: Tong Zhang, Department of Statistics, Rutgers, The State University of New Jersey
published: Aug. 13, 2008,   recorded: July 2008,   views: 3907


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.


I will present a finite-sample generalization analysis of two-part code MDL estimator. This method selects a model that minimizes the sum of the model description length plus the data description length given the model. It can be shown that under various conditions, optimal rate of convergence can be achieved through an extended family of two-part code MDL that over-penalize the model description length.

As an example, we apply MDL to learning sparse linear representations when the system dimension is much larger than the number of training examples. This is a problem that has attracted considerable attention in recent years. The generalization performance of a two-part code MDL estimator is calculated based on our theory, and it compares favorably to other methods such as 1-norm regularization.

See Also:

Download slides icon Download slides: icml08_zhang_gtt_01.pdf (140.8┬áKB)

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: