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

2;1-norm based Regression for 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

3 Author(s)
Chuan-Xian Ren ; Dept. of Math., Sun Yat-Sen Univ., Guangzhou, China ; Dao-Qing Dai ; Hong Yan

We present a novel classification method formulating an objective model by ℓ2;1-norm based regression. The ℓ2;1-norm based loss function is robust to outliers or the large variations within given data, and the ℓ2;1-norm regularization term selects correlated samples across the whole training set with grouped sparsity. This constrained optimization problem can be efficiently solved by an iterative procedure. Several benchmark data sets including facial images and gene expression data are used for evaluating the robustness and effectiveness of the new proposed algorithm, and the results show the competitive performance.

Published in:

Pattern Recognition (ACPR), 2011 First Asian Conference on

Date of Conference:

28-28 Nov. 2011

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.