A data scientist is preparing a numeric feature 'annual_income' for a linear regression model. Exploratory analysis shows the feature has several extreme high-value outliers that were confirmed to be legitimate (not data errors). The team wants to scale the feature so that these outliers do not disproportionately compress the majority of the data into a narrow range. Which scaling approach best addresses this concern?