Comparison of data reduction techniques based on the performance of SVM-type classifiers

In this work, we applied several techniques for data reduction to publicly available datasets with the goal of comparing how an increasing level of compression affects the performance of SVM-type classifiers. We consistently attained correct rates in the neighborhood of 90%, with the Principal Component Analysis (PCA) having a slight edge over the other data reduction methods (PLS, SRM, and OMP). One dataset proved to be hard to classify, even in the case of no dimensionality reduction. Also in this most challenging dataset, performing PCA was considered to offer some advantages over the other compression techniques. Based on our assessment, data reduction appears a useful tool that can provide a significant reduction in signal processing load with acceptable loss in performance.