Multiview Fisher Discriminant Analysis

author: Tom Diethe, Amazon
published: Dec. 20, 2008,   recorded: December 2008,   views: 6383


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.


CCA can be seen as a multiview extension of PCA, in which information from two sources is used for learning by finding a subspace in which the two views are most correlated. However PCA, and by extension CCA, does not use label information. Fisher Discriminant Analysis uses label information to find informative projections, which can be more informative in supervised learning settings. We show that FDA and its dual can both be formulated as generalized eigenproblems, enabling a kernel formulation. We derive a regularised two-view equivalent of Fisher Discriminant Analysis and its corresponding dual, both of which can also be formulated as generalized eigenproblems. We then show that these can be cast as equivalent disciplined convex optimisation problems, and subsequently extended to multiple views. We show experimental results on an EEG dataset and part of the PASCAL 2007 VOC challenge dataset.

See Also:

Download slides icon Download slides: lms08_diethe_mfda_01.pdf (84.0┬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: