By Topic

On the maximization of divergence in pattern recognition (Corresp.)

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
$33 $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

1 Author(s)

This correspondence considers the problem of maximization of the divergence between a pair of unequal mean and unequal covariance matrix Gaussian distributed pattern classes. The original pattern space is transformed into a new space such that the sum of the covariance matrices is a unit matrix. From this relationship, a set of orthonormal directions are obtained sequentially such that, when the patterns are projected onto each of these directions, the divergence between the pattern classes is maximized.

Published in:

IEEE Transactions on Information Theory  (Volume:22 ,  Issue: 5 )