Separation of post-nonlinear mixtures using ACE and temporal decorrelation

Ziehe, A; Kawanabe , M; Harmeling, S; Müller, K-R

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Conference Paper

Separation of post-nonlinear mixtures using ACE and temporal decorrelation

MPS-Authors

There are no MPG-Authors in the publication available

External Resource

https://www.researchgate.net/publication/2551001_Separation_Of_Post-Nonlinear_Mixtures_Using_ACE_And_Temporal_Decorrelation
(Any fulltext)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Ziehe, A., Kawanabe, M., Harmeling, S., & Müller, K.-R. (2001). Separation of post-nonlinear mixtures using ACE and temporal decorrelation. In T.-W. Lee, T. Jung, S. Makeig, & T. Sejnowski (Eds.), Third International Workshop on Independent Component Analysis and Blind Signal Separation (ICA 2001) (pp. 433-438).

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-E169-4

Abstract

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.