Similarity-Based Classifiers: Problems and Solutions

author: Maya Gupta, Department of Electrical Engineering, University of Washington
published: July 30, 2009,   recorded: June 2009,   views: 5567


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.


Similarity-based learning assumes one is given similarities between samples to learn from, and can be considered a special case of graph-based learning where the graph is given and fully-connected. Such problems arise frequently in computer vision, bioinformatics, and problems involving human judgment. We will review the field of similarity-based classification and describe the main problems encountered in adapting standard algorithms for this problem, including different approaches to approximating indefinite similarities by kernels. We will motivate why local methods lessen the indefinite similarity problem, and show that a kernelized linear interpolation and local kernel ridge regression can be profitably applied to such similarity-based classification problems by framing them as weighted nearest-neighbor classifiers. Eight real datasets will be used to compare state-of-the-art methods and illustrate the open challenges in this field.

See Also:

Download slides icon Download slides: mlss09us_gupta_sbcps_01.pptx (3.7┬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: