Title :
Nonparametric Data Reduction
Author :
Fukunaga, K. ; Mantock, J.M.
Author_Institution :
Department of Electrical Engineering, Purdue University, West Lafayette, IN 47907.
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.
Keywords :
Application software; Clustering algorithms; Databases; Indium tin oxide; Iterative algorithms; Nearest neighbor searches; Neural networks; Pattern analysis; Pattern classification; Sorting; Condensing algorithms; data reduction; nearest neighbor techniques;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.1984.4767485
