Singular Value Decomposition


Video tutorials

3:48min, SVD song

5:09min, Geometric interpreation

Matlab demonstration for a geometric interpretation of the SVD which is M=USV^T breaks down the matrix operation of M by rotation (V^T) –> scaling (S) –> rotation (U)

23:55min, Pseudoinverse + Least squares with SVD

explains two applications of the SVD
(i) computing the pseudoinverse
(ii) using the pseudoinverse for finding the least squares solution of a set of linear equations

note: before watching this lecture recapitulate, that

  • the nullspace (kernel) of a matrix is the set of all vectors that map to 0
  • the range of a matrix is the set of all possible linear combinations of its column vectors = the image of the linear mapping represented by the matrix
public/singular_value_decomposition_svd.txt · Last modified: 2013/12/21 14:39 (external edit) · []
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