Data Reduction in Support Vector Machines by a Kernelized Ionic Interaction Model

Abstract

A major drawback of support vector machines is that the computational complexity for finding an optimal solution scales as $O(n^3)$, where $n$ is the number of training data points. In this paper, we introduce a novel ionic interaction model for data reduction in support vector machines. It is applied to select data points and exclude outliers in the kernel feature space and produce a data reduction algorithm with computational complexity of about $n^3/4$ floating point operations. The instance-based learning algorithm has been successfully applied for data reduction in high dimensional feature spaces obtained by kernel functions. We also present a data reduction method based on the kernelized instance based algorithm. We test the performances of our new methods which illustrate thatthe computation time can be significantly reduced without any significant decrease in the prediction accuracy.