Cart (Loading....) | Create Account
Close category search window
 

Hub Discovery in Partial Correlation Graphs

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)
Hero, A. ; Departments of EECS, BME and Statistics, University of Michigan, Ann Arbor, U.S.A. ; Rajaratnam, B.

One of the most important problems in large-scale inference problems is the identification of variables that are highly dependent on several other variables. When dependence is measured by partial correlations, these variables identify those rows of the partial correlation matrix that have several entries with large magnitudes, i.e., hubs in the associated partial correlation graph. This paper develops theory and algorithms for discovering such hubs from a few observations of these variables. We introduce a hub screening framework in which the user specifies both a minimum (partial) correlation $rho $ and a minimum degree $delta $ to screen the vertices. The choice of $rho $ and $delta $ can be guided by our mathematical expressions for the phase transition correlation threshold $rho _{c}$ governing the average number of discoveries. They can also be guided by our asymptotic expressions for familywise discovery rates under the assumption of large number $p$ of variables, fixed number $n$ of multivariate samples, and weak dependence. Under the null hypothesis that the dispersion (covariance) matrix is sparse, these limiting expressions can be used to enforce familywise error constraints and to rank the discoveries in order of increasing statistical significance. For $nll p$, the computational complexity of the proposed partial correlation screening method is low and is therefore highly scalable. Thus, it can be applied to significantly large- problems than previous approaches. The theory is applied to discovering hubs in a high-dimensional gene microarray dataset.

Published in:

Information Theory, IEEE Transactions on  (Volume:58 ,  Issue: 9 )

Date of Publication:

Sept. 2012

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.