[Numpy-discussion] svd() and eigh()
devnew@gmai...
devnew@gmai...
Sat Mar 1 07:43:06 CST 2008
hi
i have a set of images of faces which i make into a 2d array using
numpy.ndarray
each row represents a face image
faces=
[[ 173. 87. ... 88. 165.]
[ 158. 103. .. 73. 143.]
[ 180. 87. .. 55. 143.]
[ 155. 117. .. 93. 155.]]
from which i can get the mean image =>
avgface=average(faces,axis=0)
and calculate the adjustedfaces=faces-avgface
now if i apply svd() i get
u, s, vt = linalg.svd(adjustedfaces, 0)
# a member posted this
facespace=vt.transpose()
and if i calculate covariance matrix
covmat=matrix(adjustedfaces)* matrix(adjustedfaces).transpose()
eval,evect=eigh(covmat)
evect=sortbyeigenvalue(evect) # sothat largest eval is first
facespace=evect* matrix(adjustedfaces)
what is the difference btw these 2 methods? apparently they yield
different values for the facespace. which should i follow?
is it possible to calculate eigenvectors using svd()?
thanks
D
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