Nonparametric Data Reduction

Authors:
K. Fukunaga;J. M. Mantock
Affiliations:
Department of Electrical Engineering, Purdue University, West Lafayette, IN 47907.;Department of Electrical Engineering, Purdue University, West Lafayette, IN/ Aerospace Corporation, Los Angeles, CA/ Texas Instruments, Inc., Dallas, TX.
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1984

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Abstract

A nonparametric data reduction technique is proposed. Its goal is to select samples that are ``representative'' of the entire data set. The technique is iterative and is based on the use of a criterion function and nearest neighbor density estimates. Experiments are presented to demonstrate the algorithm.