Fast Algorithms for Informed Source Separation

author: Augustin Lefèvre, Université catholique de Louvain
published: Aug. 26, 2013,   recorded: July 2013,   views: 3104
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 study a convex formulation of low-rank matrix factorization, in a special case where additional information on the factors is known. Our formulation is typically adapted to source separation scenarii, where additional information on the sources may be provided by an expert. Our formulation promotes low-rank with a nuclear-norm based penalty. As it is non-smooth, generic first-order algorithms suffer from slow convergence rates. We study and compare several algorithms that fully exploit the structure of our problem while keeping memory requirements linear in the size of the problem.

See Also:

Download slides icon Download slides: roks2013_lefevre_methods_01.pdf (513.5 KB)


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: