Effective Dimensionality Reduction Based on Support Vector Machine

Abstract

This paper presents an effective dimensionality reduction method based on support vector machine. By utilizing mapping vectors from support vector machine for dimensionality reduction purpose, we obtain features which are computationally efficient, providing high classification accuracy and robustness especially in noisy environment. These characteristics are acquired from the generalization capability of support vector machine by minimizing the structural risk. To further reduce dimensionality, this paper introduces the redundancy removal process based on an asymmetric decor relation measure with kernel function. Experimental results show that the proposed dimensionality reduction method provides the most appropriate trade off between classification accuracy and robustness in relatively low dimensional space.