How does PCA relate to Singular Value Decomposition (SVD) mathematically?

PCA can be computed using SVD of the centered data matrix.

Instead of computing covariance matrix explicitly, PCA often uses SVD: X = UΣVᵀ. Here, V columns are principal directions, and singular values relate to explained variance. This method is numerically stable and efficient for large datasets.