Is this the right way of projecting the training set into the eigespace? MATLAB
I have computed PCA using the following : function [signals,V] = pca2(data) [M,N] = size(data); data = reshape(data, M*N,1); % subtract off the mean for each dimension mn = mean(data,2); data = bsxfun(@minus, data, mean(data,1)); % construct the matrix Y Y = data'*data / (M*N-1); [V D] = eigs(Y, 10); % reduce to 10 dimension % project the original data signals = data * V; My question is: Is "signals" is the projection of the training set into the eigenspace? I saw in "Amir Hossein" code that "centered image vectors" that is "data" in the above code needs to be projected into the "facespace" by