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

A feature partitioning approach to subspace classification

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)
Vijayakumar, K. ; Vasavi Coll. of Eng., Hyderabad ; Negi, A.

In this paper we present a feature partitioning approach to subspace classification. The proposed method computes subspaces using feature partitioning approach, where each pattern is divided into sub-patterns and extract features locally from sub- patterns and combines them to compute global subspace. We prove that the proposed approach consumes significantly less time in comparison to traditional PCA based subspace methods. The superiority of proposed approach can be understood from the experimental results of feature partitioning approach to principal component analysis over traditional principal component analysis.

Published in:

TENCON 2007 - 2007 IEEE Region 10 Conference

Date of Conference:

Oct. 30 2007-Nov. 2 2007

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.