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From Bruce A Johnson <>
Subject [math] getCovariance in SingularValueDecomposition
Date Wed, 08 Oct 2014 01:59:19 GMT
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

       = 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

I don't understand why the calculation uses the inverse of the singular values.

Is that correct?


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