# Difference between Principal Component Analysis(PCA) and Singular Value Decomposition(SVD)? [duplicate]

Possible Duplicate:
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?

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