Here we have a solution, however i don't really understand it.
http://www.cs.cf.ac.uk/Dave/Papers/Pami-EIgenspace.pdf
Hello,
I am building an active appearance model. I have a large training data set. Too big. :/
My computer's RAM cannot handle a model of this size.
If i make a series of smaller PCA models. Is there a way to combine a series of PCA models into one 'averaged' projection space?
Each sample is an image, that is reshaped into a 1D vector.
Vectors are stacked into one large training data matrix, where each row is one sample. We call OpenCV's PCA function and get the 1D eigenvectors and a 2D eigenvalues(1 row per sample) matrix. We are interested in the 1D eigenvector matrix.
Somebody has suggested Structural Equation Modelling. I thought an average of the eigenvectors should work.... provided the components correspond throughout the series of models.
Any clues anyone?
Here we have a solution, however i don't really understand it.
http://www.cs.cf.ac.uk/Dave/Papers/Pami-EIgenspace.pdf