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Singular Value Decomposition (Read 3021 times)
Gerrit-Jan Linker
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Singular Value Decomposition
06.11.13 at 11:17:53
Singular Value Decomposition
SVD is based on a theorem from linear algebra which says that any rectangular matrix S can be brought into diagonal form with two unitary matrices U and V.
so that S'ij=S'jjδij
Singular value decomposition (SVD) can be looked at from three mutually compatible points of view:
  • SVD can be seen as a method for transforming correlated variables into a set of uncorrelated ones that better expose the various relationships among the original data items.  
  • SVD is a method for identifying and ordering the dimensions along which data points exhibit the most variation.  
  • SVD can be seen as a method for data reduction. Once it is identi
    ed where the most variation is, it's possible to  
    find the best approximation of the original data points using fewer dimensions.  

Ria Broer, INTERNATIONAL    JOURNAL   OF  QUANTUM    CHEMISTRY,   VOL.  45, 587-590   (1993) l.pdf
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« Last Edit: 07.11.13 at 10:54:58 by Gerrit-Jan Linker »  

Gerrit-Jan Linker
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