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Proceedings Paper

Clustering and compression of high-dimensional sensor data
Author(s): David J. Hermann; Stanley C. Ahalt; Richard A. Mitchell
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Paper Abstract

We investigate the compression of high-dimensional sensor data using vector quantization. Two metrics are presented for compression with the frequency sensitive competitive learning (FSCL) vector quantization (VQ) algorithm, and several indices of partitional validity are used to analyze the resulting VQ codebook clusters. Cluster analysis is used to determine the compressibility of the data. The results of this cluster analysis will help determine the effect of data compression on the performance of a target recognition system.

Paper Details

Date Published: 3 September 1993
PDF: 12 pages
Proc. SPIE 1955, Signal Processing, Sensor Fusion, and Target Recognition II, (3 September 1993); doi: 10.1117/12.154985
Show Author Affiliations
David J. Hermann, Battelle Memorial Institute (United States)
Stanley C. Ahalt, The Ohio State Univ. (United States)
Richard A. Mitchell, Air Force Wright Lab. (United States)


Published in SPIE Proceedings Vol. 1955:
Signal Processing, Sensor Fusion, and Target Recognition II
Ivan Kadar; Vibeke Libby, Editor(s)

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