Blind separation of post-nonlinear mixtures using gaussianizing transformations 
and temporal decorrelation

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

Datensatz

DATENSATZ AKTIONENEXPORT

Zur Ablage hinzufügen

Lokale TagsFreigabegeschichteDetailsÜbersicht

Freigegeben

Konferenzbeitrag

Blind separation of post-nonlinear mixtures using gaussianizing transformations and temporal decorrelation

MPG-Autoren

Es sind keine MPG-Autoren in der Publikation vorhanden

Externe Ressourcen

http://www.kecl.ntt.co.jp/icl/signal/ica2003/
(Verlagsversion)

Volltexte (beschränkter Zugriff)

Für Ihren IP-Bereich sind aktuell keine Volltexte freigegeben.

Volltexte (frei zugänglich)

Es sind keine frei zugänglichen Volltexte in PuRe verfügbar

Ergänzendes Material (frei zugänglich)

Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar

Zitation

Ziehe, A., Kawanabe, M., Harmeling, S., & Müller, K.-R. (2003). Blind separation of post-nonlinear mixtures using gaussianizing transformations and temporal decorrelation. In S.-I. Amari, A. Cichocki, S. Makino, & N. Murata (Eds.), Fourth International Symposium on Independent Component Analysis and Blind Signal Separation (ICA 2003) (pp. 269-274).

Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-DCB5-E

Zusammenfassung

At the previous workshop (ICA2001) we proposed the ACE-TD method that reduces the post-nonlinear blind source separation problem (PNL BSS) to a linear BSS problem. The method utilizes the Alternating Conditional Expectation (ACE) algorithm to approximately invert the (post-)non-linear functions. In this contribution, we propose an alternative procedure called Gaussianizing transformation, which is motivated by the fact that linearly mixed signals before nonlinear transformation are approximately Gaussian distributed. This heuristic, but simple and efficient procedure yields similar results as the ACE method and can thus be used as a fast and effective equalization method. After equalizing the nonlinearities, temporal decorrelation separation (TDSEP) allows us to recover the source signals. Numerical simulations on realistic examples are performed to compare "Gauss-TD" with "ACE-TD".