Correlation-Based Robust Linear Regression with Iterative Outlier Removal

Here we consider linear regression from the view of correlation and propose a robust regression algorithm. The main idea of this work is from the fact that the inliers lying in a low dimensional subspace are mostly correlated, and the presence of outliers leads to the decrease of correlation. We design an iterative outlier removal algorithm based on correlation, by which the outliers can be effectively removed in a normal-distributed or uniform-distributed data set. Finally, the linear equation is calculated based on the remaining points. The experiment results show that the proposed method outperforms the state-of-the-art approaches. In some cases in which outliers are more than inliers, the proposed method can still obtain the real formulas.