Research of PCA and KPCA in the characteristics simplicity of the gene data

Although SVM method is suitable for high dimensional data analysis, gene expression data are often thousands of dimensions, the time cost of this algorithm is quite high. The method of dimension reduction based on principal component analysis (PCA) and kernel principal component analysis (KPCA) can not only shorten the algorithm´s running time, but also integrate the useful characteristics information. This paper compares the classification accuracy of the search range with different parameters between PCA-SVM and KPCA-SVM when the cumulative contribution rate reaches 100%, 95% and 90% by the experimental data of the Singh-2002. The experimental results show that the change of classification accuracy based on PCA-SVM is none of the business of the change of the cumulative contribution rate, but change of classification accuracy based on KPCA-SVM decreases or remains unchanged with the loss of the cumulative contribution.