A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine Export

@article{citeulike:820287, abstract = {Recently, support vector machine (SVM) has become a popular tool in time series forecasting. In developing a successful SVM forecastor, 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. PCA linearly transforms the original inputs into new uncorrelated features. 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 KPCA feature extraction, followed by ICA feature extraction.}, author = {Cao, L. J. and Chua, K. S. and Chong, W. K. and Lee, H. P. and Gu, Q. M.}, booktitle = {Support Vector Machines}, citeulike-article-id = {820287}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/S0925-2312(03)00433-8}, citeulike-linkout-1 = {http://www.sciencedirect.com/science/article/B6V10-4991TN5-1/2/96c2949bf0cb967ce8d5ed2ceb40597a}, comment = {Este artigo faz um comparativo entre v\'{a}rias t\'{e}cnicas de extracao de caracteristicas}, doi = {10.1016/S0925-2312(03)00433-8}, journal = {Neurocomputing}, keywords = {ica, kernel, kpca, pca}, month = {September}, number = {1-2}, pages = {321--336}, posted-at = {2006-08-29 02:14:35}, priority = {3}, title = {A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine}, url = {http://dx.doi.org/10.1016/S0925-2312(03)00433-8}, volume = {55}, year = {2003} }

TY - JOUR ID - citeulike:820287 L3 - citeulike-article-id:820287 N2 - Recently, support vector machine (SVM) has become a popular tool in time series forecasting. In developing a successful SVM forecastor, 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. PCA linearly transforms the original inputs into new uncorrelated features. 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 KPCA feature extraction, followed by ICA feature extraction. IS - 1-2 JF - Neurocomputing EP - 336 TI - A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine VL - 55 T2 - Support Vector Machines SP - 321 KW - ica KW - kernel KW - kpca KW - pca N1 - Este artigo faz um comparativo entre várias técnicas de extracao de caracteristicas AU - Cao, LJ AU - Chua, KS AU - Chong, WK AU - Lee, HP AU - Gu, QM PY - 2003/09// UR - http://dx.doi.org/10.1016/S0925-2312(03)00433-8 ER -