A Declustering Criterion for Feature Extraction in Pattern Recognition

Authors:
J. Fehlauer;B. A. Eisenstein
Affiliations:
Drexel University;-
Venue:
IEEE Transactions on Computers
Year:
1978

Citing 4
Cited 2

Decision-making processes in pattern recognition (ACM monograph series)

Decision-making processes in pattern recognition (ACM monograph series)
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Application of the Karhunen-Loève Expansion to Feature Selection and Ordering

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A Comparison of Seven Techniques for Choosing Subsets of Pattern Recognition Properties

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Recent Developments in Pattern Recognition

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Tool wear monitoring using artificial neural network based on extended Kalman filter weight updation with transformed input patterns

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Abstract

A feature extraction technique based on a new criterion for "declustering" is presented. Declustering occurs when sample vectors from one pattern class form a densely packed point constellation, or cluster, in feature space while vectors from another class do not form a cluster but instead array themselves as outliers. Features chosen to optimize the declustering criterion enhance class separation and are robust over a wide range of measurement statistics.