Real-world datasets often have outliers that can mess up data analysis. A new method called robust regression was created to handle this issue. It finds the biggest subset of data that can be approximated well with a simple linear model. This method, called SLISE, is fast and works great on high-dimensional datasets. It was tested on both fake and real data, showing it can handle outliers well.