What is the role of centering data before applying PCA?

Centering ensures PCA captures variance around the mean rather than absolute values.

PCA requires subtracting the mean of each feature so that covariance reflects variability around the origin. Without centering, the first principal component may incorrectly align with the mean offset instead of true variance direction. This leads to misleading components.