Summary
SVD
be careful of the number of rows and cols
Process
Concept
Singular matrices
- transformation that may add or remove dimensions
Singular value decomposition
- diagonalization of non-square(and singular) matrcies
- using the product with transpose to create a symmetric matrix, on which orthogonal diagonalization can be done
we want
to be like the psuedo-eigenvectors of
Relation to polar decomposition
Application
SVD by transpose product
pain
Extra
Octave
octave
# SVD of a matrix
[U, S, V] = svd(A)