Datensatz

Zusammenfassung

We propose an efficient method based on the concept of maximal correlation that reduces the post-nonlinear blind
source separation problem (PNL BSS) to a linear BSS problem.
For this we apply the Alternating Conditional Expectation
(ACE) algorithm – a powerful technique from nonparametric
statistics – to approximately invert the (post-)nonlinear
functions. Interestingly, in the framework of the ACE
method convergence can be proven and in the PNL BSS
scenario the optimal transformation found by ACE will coincide
with the desired inverse functions. After the nonlinearities
have been removed by ACE, temporal decorrelation
(TD) allows us to recover the source signals. An excellent
performance underlines the validity of our approach
and demonstrates the ACE-TD method on realistic examples.