You're working on a high-dimensional dataset with many redundant features. Which feature selection methods might help reduce the dimensionality while maintaining the essential information?

Principal Component Analysis (PCA) is a dimensionality reduction technique that can be used when dealing with high-dimensional datasets with many redundant features. PCA transforms the original features into a new set of uncorrelated features, capturing the most variance in the data, thus helping to maintain the essential information while reducing the dimensionality.