NASA Logo

NTRS

NTRS - NASA Technical Reports Server

Back to Results
Decision Boundary Feature Extraction for Nonparametric ClassificationFeature extraction has long been an important topic in pattern recognition. Although many authors have studied feature extraction for parametric classifiers, relatively few feature extraction algorithms are available for nonparametric classifiers. A new feature extraction algorithm based on decision boundaries for nonparametric classifiers is proposed. It is noted that feature extraction for pattern recognition is equivalent to retaining 'discriminantly informative features' and a discriminantly informative feature is related to the decision boundary. Since nonparametric classifiers do not define decision boundaries in analytic form, the decision boundary and normal vectors must be estimated numerically. A procedure to extract discriminantly informative features based on a decision boundary for non-parametric classification is proposed. Experiments show that the proposed algorithm finds effective features for the nonparametric classifier with Parzen density estimation.
Document ID
19970022622
Acquisition Source
Headquarters
Document Type
Reprint (Version printed in journal)
External Source(s)
Authors
Lee, Chulhee
(Purdue Univ. West Lafayette, IN United States)
Landgrebe, David A.
(Purdue Univ. West Lafayette, IN United States)
Date Acquired
August 17, 2013
Publication Date
April 1, 1993
Publication Information
Publication: IEEE Transactions on Systems, Man, and Cybernetics
Publisher: IEEE
Volume: 23
Issue: 2
ISSN: 0018-9472
Subject Category
Earth Resources And Remote Sensing
Report/Patent Number
NASA-CR-204336
NAS 1.26:204336
Accession Number
97N72135
Funding Number(s)
CONTRACT_GRANT: NAGw-925
Distribution Limits
Public
Copyright
Public Use Permitted.
Document Inquiry

Available Downloads

There are no available downloads for this record.
No Preview Available