What is the intuitive relationship between SVD and PCA
I am confused between PCA and SVD.
The wikipedia page for the PCA has this line:
"PCA can be done by eigenvalue decomposition of a data covariance matrix or singular value decomposition of a data matrix, usually after mean centering the data for each attribute."
Does this mean that PCA = SVD of a data matrix?
Is there an article/tutorial that explains the difference?