An empirical study of dimensionality reduction in support vector machine

Title:

An empirical study of dimensionality reduction in support vector machine

Creator:

Cao, L. J., JingQing , Zhang, Zongwu, Cai, and Guan, Liam Kian

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:f4506455-91f4-4ccb-9997-7a06e63b20d6
uuid:f4506455-91f4-4ccb-9997-7a06e63b20d6

Subject:

Support vector machines, principal component analysis, kernel principal component analysis, and independent component analysis

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

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.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2006 Volume:16 | Number:3

Harvested from:

CDK

Metadata only:

false