Principal Component Analysis in Face Recognition - number of Eigenvalues? I'm at the beginning of learning about the PCA as it is applied in the field of face recognition (Eigenface algorithm) and I came about the following question:
"You're using a training set of 80 images (150x150 pixels). After visualizing the Eigenvalues you decide to keep 40% of the Eigenvectors. What dimension do the resulting projected images (template) have?"
Now, since I think the number of Eigenvalues calculated from a data set is equal to the number of dimensions of that data set, I'd say you'd get images with 9000 dimensions because the dimension of the training images is 150x150=22500 and I'd keep 40% of those.
So is this assumtion correct? Or does the number of Eigenvalues differ from the dimension of the input images ?
Thank you, if you need clarification on the question, just ask.

Not sure but I think the number of eigenvalues or eigenvectors you get is dependent on the number of images so it would be 0.4 * 80 = 32 of them. The images are "flattened" so you do the covariance and then eigenvector/eigenvalue calcultions on an array that has 80 rows and 150*150 columns so you end up with a square symmetric covariance matrix that is 80 by 80, then do the eigen calculations on that matrix to get a maximum of 80 eigenvectors/eigenvalues.
