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
Citation
Cao, Lijuan; Zhang, Jingqing; Cai, Zongwu; and Lim, Kian Guan.
An Empirical Study of Dimensionality Reduction in Support Vector Machine. (2006). Neural Network World. 16, 177-192.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/2449