Clustering and compression of high-dimensional sensor data

David J. Hermann; Stanley C. Ahalt; Richard A. Mitchell

doi:10.1117/12.154985

3 September 1993 Clustering and compression of high-dimensional sensor data

David J. Hermann, Stanley C. Ahalt, Richard A. Mitchell

Proceedings Volume 1955, Signal Processing, Sensor Fusion, and Target Recognition II; (1993) https://doi.org/10.1117/12.154985
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

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.

Citation Download Citation

David J. Hermann, Stanley C. Ahalt, and Richard A. Mitchell "Clustering and compression of high-dimensional sensor data", Proc. SPIE 1955, Signal Processing, Sensor Fusion, and Target Recognition II, (3 September 1993); https://doi.org/10.1117/12.154985

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