By Topic

Eigenvalue Properties of Projection Operators and Their Application to the Subspace Method of Feature Extraction

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

1 Author(s)
Therrien, C.W. ; Massachusetts Institute of Technology Lincoln Laboratory

The subspace method proposed by Watanabe et al. is an important method of feature extraction in pattern recognition. Some new results described here provide a formulation of the subspace method that is particularly easy to implement and overcomes the various numerical difficulties that were inherent in earlier implementations of the method. The present formulation is also important in that it provides the key to the relation of this method to other methods of feature extraction.

Published in:

Computers, IEEE Transactions on  (Volume:C-24 ,  Issue: 9 )