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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]:
non-linearities g1(.) and g2(.) are applied separately on every
component of the vector ,
identity matrix could be replaced by any positive definite diagonal matrix.
In [91,28], the following algorithm called the EASI
algorithm was introduced:
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.