How does PCA interact with feature correlation structures in datasets?

PCA captures correlated features by combining them into shared principal components.

When features are correlated, they contain redundant information. PCA identifies these correlations through covariance structure and compresses them into fewer components that represent shared variance directions, effectively decorrelating the dataset.