Comparison of data reduction techniques based on SVM classifier and SVR performance

Dave Zhao; Ramona Georgescu; Peter Willett

doi:10.1117/12.894446

16 September 2011 Comparison of data reduction techniques based on SVM classifier and SVR performance

Dave Zhao, Ramona Georgescu, Peter Willett

Proceedings Volume 8137, Signal and Data Processing of Small Targets 2011; 81370X (2011) https://doi.org/10.1117/12.894446
Event: SPIE Optical Engineering + Applications, 2011, San Diego, California, United States

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.

Citation Download Citation

Dave Zhao, Ramona Georgescu, and Peter Willett "Comparison of data reduction techniques based on SVM classifier and SVR performance", Proc. SPIE 8137, Signal and Data Processing of Small Targets 2011, 81370X (16 September 2011); https://doi.org/10.1117/12.894446

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