Scheduled System Maintenance on May 29th, 2015:
IEEE Xplore will be upgraded between 11:00 AM and 10:00 PM EDT. During this time there may be intermittent impact on performance. We apologize for any inconvenience.
By Topic

On the Use of the Chi-Squared Distance for the Structured Learning of Graph Embeddings

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Haifeng Zhao ; Dept. of Comp. Sci. & Eng., Nanjing Univ. of Sci. & Technol., Nanjing, China ; Robles-Kelly, A. ; Jun Zhou

In this paper, we describe the use of concepts from the areas of structural and statistical pattern recognition for the purposes of recovering a mapping which can be viewed as an operator on the graph attribute-set. This mapping can be used to embed graphs into spaces where tasks such as classification and retrieval can be effected. To do this, we depart from concepts in graph theory so as to introduce mappings as operators over graph spaces. This treatment leads to the recovery of a mapping based upon the graph attributes which is related to the edge-space of the graphs under study. As a result, the recovered mapping is a linear operator over the attribute set which is associated with the graph topology. To recover this mapping, we employ an optimisation approach whose cost function is based upon the Chi-squared distance and is related to the target function used in discrete Markov Random Field approaches. Thus, the method presented here provides a link between concepts in graph theory, statistical inference and linear operators. We illustrate the utility of the recovered embedding for purposes of shape categorisation and retrieval. We also compare our results to those yielded by alternatives.

Published in:

Digital Image Computing Techniques and Applications (DICTA), 2011 International Conference on

Date of Conference:

6-8 Dec. 2011