I've been trying to implement the CPU GS-PCA algorithm in this paper .
The code starts on page 28
I have a program written a script in python which gives the same output as the C++ program in the paper.
My function looks like this
[T, P, R, L, U] = IterativePCA(theData, numDimensions, 10000, 1.0e-7)
theData is the big matrix with the data vectors
numDimensions is the number of dimensions the PCA algorithm projects onto
I'm using 10000 max iterations
and an error tolerance of 1.0e-7
But I still have a question
What are the matricies
[T, P, R, L, U] ?
How do I get from them to the new data that has been projected onto the PCA basis?
I can compute this data using
from sklearn.decomposition import PCA PCA(n_components=n_numDimensions).fit_transform(theData)
But I want to be able to do it using the iterative method in the paper