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Proceedings Paper

Comparison of data reduction techniques based on SVM classifier and SVR performance
Author(s): Dave Zhao; Ramona Georgescu; Peter Willett
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Paper Abstract

In this work, we applied several data compression techniques to simulated data and the Turbofan engine degradation simulation data set from NASA, with the goal of comparing their performance when coupled with the Support Vector Machine (SVM) classifier and the SVM regression (SVR) predictor. We consistently attained correct rates in the neighborhood of 90% for simulated data set, with the Principal Component Analysis (PCA), Sparse Reconstruction by Separable Approximation (SpaRSA) and Partial Least Squares (PLS) having a slight edge over the other data reduction methods for data classification. We achieved 22% error rate with SRM for the Turbofan data set 1 and 40% error rate with PCA for Turbofan data set 2. Throughout the tests we have performed, PCA proved to be the best data reduction method.

Paper Details

Date Published: 17 September 2011
PDF: 15 pages
Proc. SPIE 8137, Signal and Data Processing of Small Targets 2011, 81370X (17 September 2011); doi: 10.1117/12.894446
Show Author Affiliations
Dave Zhao, Univ. of Connecticut (United States)
Ramona Georgescu, Univ. of Connecticut (United States)
Peter Willett, Univ. of Connecticut (United States)


Published in SPIE Proceedings Vol. 8137:
Signal and Data Processing of Small Targets 2011
Oliver E. Drummond, Editor(s)

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