Appendix CMatrix Decompositions
C.1 Singular‐Value Decomposition
The singular‐value decomposition (SVD) is an integral part of subspace identification methods 1–4. It is characterized by an orthogonal decomposition of a rectangular 
matrix 
(C.1)
where the matrices 
and 
are orthogonal such that 
, 
along with the diagonal matrix, 
with 
. The set of ordered singular values are given by 
with the property that . The singular values of and the respective column and row vectors of and , and are ...
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