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 _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion