The major difference between the IPCA-ICA algorithm and
the PCA-ICA batch algorithm is the real-time sequential process.
IPCA-ICA does not need a large memory to store the
whole datamatrix that represents the incoming images. Thus
in each step, this function deals with one incoming image
in order to update the estimated non-Gaussian directions,
and the next incoming image can be stored over the previous
one. The first estimated non-Gaussian vectors (corresponding
to the largest eigenvalues) in IPCA correspond to the vectors
that carry the most efficient information. As a result, the
processing of IPCA-ICA can be restricted to only a specified
number of first non-Gaussian directions. On the other side,
the decision of efficient vectors in PCA can be done only after
calculating all the vectors, so the program will spend a
certain time calculating unwanted vectors. Also, ICA works
usually in a batch mode where the extraction of independent
components of the input eigenvectors can be done only when.