Title

An Empirical Study of Dimensionality Reduction in Support Vector Machine

Publication Type

Journal Article

Publication Date

6-2006

Abstract

Recently, the the support vector machine (SVM) has become a popular tool in time series forecasting. In developing a successful SVM forecaster, the first step is feature extraction. This paper proposes the applications of principal component analysis (PCA), kernel principal component analysis (KPCA) and independent component analysis (ICA) to SVM for feature extraction. The PCA linearly transforms the original inputs into new uncorrelated features. The KPCA is a nonlinear PCA developed by using the kernel method. In ICA, the original inputs are linearly transformed into features which are mutually statistically independent. By examining the sunspot data, Santa Fe data set A and five real futures contracts, the experiment shows that SVM by feature extraction using PCA, KPCA or ICA can perform better than that without feature extraction. Furthermore, among the three methods, there is the best performance in the KPCA feature extraction, followed by the ICA feature extraction.

Discipline

Portfolio and Security Analysis

Research Areas

Finance

Publication

Neural Network World

Volume

16

First Page

177

Last Page

192

ISSN

1210-0552