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)

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) · []