Fluid dynamics models for low rank discriminant analysis

author: Yung-Kyun Noh, Seoul National University
published: June 3, 2010,   recorded: May 2010,   views: 282
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 consider the problem of reducing the dimensionality of labeled data for classification. Unfortunately, the optimal approach of finding the low-dimensional projection with minimal Bayes classification error is intractable, so most standard algorithms optimize a tractable heuristic function in the projected subspace. Here, we investigate a physics-based model where we consider the labeled data as interacting fluid distributions. We derive the forces arising in the fluids from information theoretic potential functions, and consider appropriate low rank constraints on the resulting acceleration and velocity flow fields. We show how to apply the Gauss principle of least constraint in fluids to obtain tractable solutions for low rank projections. Our fluid dynamic approach is demonstrated to better approximate the Bayes optimal solution on Gaussian systems, including infinite dimensional Gaussian processes.

See Also:

Download slides icon Download slides: aistats2010_noh_fdmfl_01.pdf (1.6 MB)

Download slides icon Download slides: aistats2010_noh_fdmfl_01.ppt (3.6 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: