> i read in some document on the topic of eigenfaces that > 'Multiplying the sorted eigenvector with face vector results in > getting the > face-space vector' > facespace=sortedeigenvectorsmatrix * adjustedfacematrix > (when these are numpy.matrices ) This will not work with numpy matrices.* is elementwise mult.
> that is why the confusion about transposing X inside > > facespace=dot(X.T,u[:,reorder]) > > if i make matrices out of sortedeigenvectors, adjustedfacematrix > then > i will get facespace =sortedeigenvectorsmatrix * adjustedfacematrix > which has a different set of elements than that obtained by > dot(X.T, u[:,reorder]). No, they are the same. u[:, reorder] *is* the sortedeigenvectormatrix, and the transpose of a matrixproduct: (A*B).T == B.T*A, so your facespace is just the transpose of mine. I dont know why you are getting the end result wrong. Perhaps you are reshaping wrong? I'll try a complete example :-) Get example data: http://www.cs.toronto.edu/~roweis/data/frey_rawface.mat ----- import scipy as sp from matplotlib.pyplot import * fn = "frey_rawface.mat" data = sp.asarray(sp.io.loadmat(fn)['ff'], dtype='d').T data = data - data.mean(0) u, s, vt = sp.linalg.svd(data, 0) # plot the first 6 eigenimages for i in range(6): subplot(2,3,i+1), imshow(vt[i].reshape((28,20)), cmap=cm.gray) axis('image'), xticks([]), yticks([]) title("First 6 eigenfaces") ------ Arnar _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
