Robust Locally Weighted Regression Algorithm

The Robust Locally Weighted Regression algorithm, introduced by Cleveland[7], utilizes local observation data to perform polynomial-weighted fitting for target points and employs least squares estimation. This algorithm combines traditional local polynomial fitting, locally weighted regression, and a robust fitting procedure with strong resistance to outliers.