Non-sparse ϵ-insensitive support vector regression for outlier detection

Abstract

To estimate the approximate relationship between the dependent variable and its independent variables, it is necessary to diagnose outliers commonly present in numerous real applications before constructing the model. Nevertheless, the techniques of the standard support vector regression (-SVR) and modified support vector regression () achieved good performance for outliers' detection for nonlinear functions with high-dimensional inputs. However, they still suffer from the costs of time and the setting of parameters. In this study, we propose a practical method for detecting outliers, using non-sparse -SVR, which minimizes time cost and introduces fixed parameters. We apply this approach for real and simulation data sets to test its effectiveness.