By Topic

Feature extraction using random matrix theory approach

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

2 Author(s)
Rojkova, V. ; Univ. of Louisville, Louisville ; Kantardzic, M.

Feature extraction involves simplifying the amount of resources required to describe a large set of data accurately. In this paper, we propose to broaden the feature extraction algorithms with Random Matrix Theory methodology. Testing the cross-correlation matrix of variables against the null hypothesis of random correlations, we can derive characteristic parameters of the system, such as boundaries of eigenvalue spectra of random correlations, distribution of eigenvalues and eigenvectors of random correlations, inverse participation ratio and stability of eigenvectors of non-random correlations. We demonstrate the usefullness of these parameters for network traffic application, in particular, for network congestion control and for detection of any changes in the stable traffic dynamics.

Published in:

Machine Learning and Applications, 2007. ICMLA 2007. Sixth International Conference on

Date of Conference:

13-15 Dec. 2007