The procedure giving faster convergence derived in previous sections
is a general approach and FastICA was seen to be a special case. Since
the faster convergence was achieved by comparing the re-estimation
step to Gaussian noise removal, the approach is valid for any
situation where the general noisy ICA model holds with Gaussian noise
and linear mixtures. It is not required that the E-step uses the
approximation
;
instead, it can be any method that can use
**s**_{0} to
compute
.
Denote this estimation by

Then it is always possible to replace the source with Gaussianized source

Having estimated two sets of sources, we can apply any method whatsoever to estimate the mixing matrix using the newly estimated sources. This gives us two new estimates of the vectors and of the mixing matrix. The final estimate is obtained as the normalized difference as above.