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

The K-L Expansion as an Effective Feature Ordering Technique for Limited Training Sample Size

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)
Muasher, Marwan Jamil ; University of Petroleum and Minerals, Dhahran, Saudi Arabia ; Landgrebe, D.A.

An effective feature ordering technique is experimentally studied in cases where the number of training samples is limited in classifying multivariate two-class normal distributions. Several experimental results on the Hughes phenomenon using this ordering technique are presented, particularly in situations where the number of training samples is only slightly higher than the number of dimensions.

Published in:

Geoscience and Remote Sensing, IEEE Transactions on  (Volume:GE-21 ,  Issue: 4 )

Date of Publication:

Oct. 1983

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.