Closed-form Supervised Dimensionality Reduction with Generalized Linear Models

author: Irina Rish, IBM Thomas J. Watson Research Center
published: Aug. 29, 2008,   recorded: July 2008,   views: 4710


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 propose a family of supervised dimensionality reduction (SDR) algorithms that combine feature extraction (dimensionality reduction) with learning a predictive model in a unified optimization framework, using data- and class-appropriate generalized linear models (GLMs), and handling both classification and regression problems. Our approach uses simple closed-form update rules and is provably convergent. Promising empirical results are demonstrated on a variety of high-dimensional datasets.

See Also:

Download slides icon Download slides: icml08_rish_cfsdr_01.pdf (2.8 MB)

Download slides icon Download slides: icml08_rish_cfsdr_01.ppt (5.9 MB)

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: