As I understand it (which could easily be wrong), calculation of the covariance
(X'X) via SVD follows the following logic:
X = USV' (via SVD, the X' indicates transpose)
X'X = (USV')' USV'
this reduces to
X'X = VSU'USV'
= V S S V'
In the SingularValueDecomposition class the covariance is calculated as:
V × J × VT where J is the diagonal matrix of the inverse of the squares of the
singular values
I don't understand why the calculation uses the inverse of the singular values.
Is that correct?
Bruce
