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

Generalization Performance of Fisher Linear Discriminant Based on Markov Sampling

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

5 Author(s)
Bin Zou ; Fac. of Math. & Comput. Sci., Hubei Univ., Wuhan, China ; Luoqing Li ; Zongben Xu ; Tao Luo
more authors

Fisher linear discriminant (FLD) is a well-known method for dimensionality reduction and classification that projects high-dimensional data onto a low-dimensional space where the data achieves maximum class separability. The previous works describing the generalization ability of FLD have usually been based on the assumption of independent and identically distributed (i.i.d.) samples. In this paper, we go far beyond this classical framework by studying the generalization ability of FLD based on Markov sampling. We first establish the bounds on the generalization performance of FLD based on uniformly ergodic Markov chain (u.e.M.c.) samples, and prove that FLD based on u.e.M.c. samples is consistent. By following the enlightening idea from Markov chain Monto Carlo methods, we also introduce a Markov sampling algorithm for FLD to generate u.e.M.c. samples from a given data of finite size. Through simulation studies and numerical studies on benchmark repository using FLD, we find that FLD based on u.e.M.c. samples generated by Markov sampling can provide smaller misclassification rates compared to i.i.d. samples.

Published in:

Neural Networks and Learning Systems, IEEE Transactions on  (Volume:24 ,  Issue: 2 )

Date of Publication:

Feb. 2013

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.