Graph Embedding in Vector Spaces by Means of Prototype Selection

author: Kaspar Riesen, University of Bern
published: July 12, 2007,   recorded: June 2007,   views: 827
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

The field of statistical pattern recognition is characterized by the use of feature vectors for pattern representation, while strings or, more generally, graphs are prevailing in structural pattern recognition. In this paper we aim at bridging the gap between the domain of feature based and graph based object representation. We propose a general approach for transforming graphs into n-dimensional real vector spaces by means of prototype selection and graph edit distance computation. This method establishes the access to the wide range of procedures based on feature vectors without loosing the representational power of graphs. Through various experimental results we show that the proposed method, using graph embedding and classification in a vector space, outperforms the tradional approach based on k-nearest neighbor classification in the graph domain.

See Also:

Download slides icon Download slides: riesen_kaspar_gevs_01.pdf (4.6┬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 !

Reviews and comments:

Comment1 chl, July 12, 2007 at 5:19 p.m.:

the right set of slides is that:

http://www.iam.unibe.ch/~riesen/Site/...

Write your own review or comment:

make sure you have javascript enabled or clear this field: