You have a large m x n data matrix M. You decide you want to perform dimension
reduction/clustering on your data and have decide to use the singular value decomposition (SVD;
also called principal components analysis PCA)
Refer to the passage above.
What represents the SVD of the Matrix standard M given the following information:
U is m x m unitary
V is n x n unitary
S is m x n diagonal
Q is n x n invertible
D is n x n diagonal
L is m x m lower triangular
U is m x m upper triangular

A.
M = U S V

B.
M = U P

C.
M = Q D Q-1

D.
M = L U