Further algorithms for canceling non-linear cross-correlations were
introduced independently in
[34,33,30] and [91,28].
Compared to the Jutten-Hérault algorithm, these algorithms reduce
the computational overhead by avoiding any matrix
inversions, and improve its stability. For example, the following
algorithm was given in [34,33]:

where , the non-linearities

A principled way of choosing the non-linearities used in these learning rules is provided by the maximum likelihood (or infomax) approach as described in the next subsection.