An Online Learning Algorithm for Bilinear Models

author: Yuanbin Wu, Department of Computer Science and Technology, East China Normal University
published: Sept. 27, 2015,   recorded: July 2015,   views: 1711

See Also:

Download slides icon Download slides: icml2015_wu_bilinear_models_01.pdf (266.8┬áKB)

Help icon Streaming Video Help

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.


We investigate the bilinear model, which is a matrix form linear model with the rank 1 constraint. A new online learning algorithm is proposed to train the model parameters. Our algorithm runs in the manner of online mirror descent, and gradients are computed by the power iteration. To analyze it, we give a new second order approximation of the squared spectral norm, which helps us to get a regret bound. Experiments on two sequential labelling tasks give positive results.

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: