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Feature Extraction Based on Decision BoundariesIn this paper, a novel approach to feature extraction for classification is proposed based directly on the decision boundaries. We note that feature extraction is equivalent to retaining informative features or eliminating redundant features; thus, the terms 'discriminantly information feature' and 'discriminantly redundant feature' are first defined relative to feature extraction for classification. Next, it is shown how discriminantly redundant features and discriminantly informative features are related to decision boundaries. A novel characteristic of the proposed method arises by noting that usually only a portion of the decision boundary is effective in discriminating between classes, and the concept of the effective decision boundary is therefore introduced. Next, a procedure to extract discriminantly informative features based on a decision boundary is proposed. The proposed feature extraction algorithm has several desirable properties: (1) It predicts the minimum number of features necessary to achieve the same classification accuracy as in the original space for a given pattern recognition problem; and (2) it finds the necessary feature vectors. The proposed algorithm does not deteriorate under the circumstances of equal class means or equal class covariances as some previous algorithms do. Experiments show that the performance of the proposed algorithm compares favorably with those of previous algorithms.
Document ID
19970023692
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 Pattern Analysis and Machine Intelligence
Publisher: Inst. of Electrical and Electronics Engineers
Volume: 15
Issue: 4
ISSN: 0162-8828
Subject Category
Earth Resources And Remote Sensing
Report/Patent Number
NASA-CR-204337
IEEE-LN-9206555
NAS 1.26:204337
Accession Number
97N72207
Funding Number(s)
CONTRACT_GRANT: NAGw-925
Distribution Limits
Public
Copyright
Public Use Permitted.
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