Low-rank modeling

author: Emmanuel Candes, Department of Statistics, Stanford University
published: Oct. 12, 2011,   recorded: September 2011,   views: 4383


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.


Inspired by the success of compressive sensing, the last three years have seen an explosion of research in the theory of low-rank modeling. By now, we have results stating that it is possible to recover certain low-rank matrices from a minimal number of entries -- or of linear functionals -- by tractable convex optimization. We further know that these methods are robust vis a vis additive noise and even outliers. In a different direction, researchers have developed computationally tractable methods for clustering high-dimensional data points that are assumed to be drawn from multiple low-dimensional linear subspaces. This talk will survey some exciting results in these areas.

See Also:

Download slides icon Download slides: mlss2011_candes_lowrank_01.pdf (15.3┬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: