A preprocessing of outlier using KERNEL PCA and factor scores in regression model

Data analysis including outlier is more difficult to the analysis without outlier. The outlier has a chance to increase the misclassification rate and the variance of estimate in the supervised learning like classification and regression. Also the outlier becomes a cluster in the clustering as unsupervised learning. So we are hard to represent the clustering result. Because of the previous problems, it is removed generally for constructing model in data mining. But when the outlier has some information on given data, we must not remove it from training data set. In this paper, using kernel PCA (principal component analysis) and factor scores, we propose a preprocessing method to contain the outlier in the modeling. The outlier effect of given training data set is reduced by the values of kernel PCA and factor scores. We verify improved performance of our work by the experimental results using simulation data sets in regression model.