svd
Singular value decomposition
s = svd(X)
- It returns the singular values of
X
. X
should be a matrix, which should not be empty, and should contain noInf
orNan
values.- If
X
hasm
rows andn
columns,s
is a vector ofmin(m, n)
elements sorted in descending order.
[U, S, V] = svd(X)
U
,S
andV
are such thatU*S*V'
is equal toX
.- If
X
is m-by-n, thenU
is m-by-m,S
m-by-n, andV
n-by-n. - The first
min(m, n)
diagonal elements ofS
contains the singular values. U*U'
andV*V'
are identity arrays.