Blind source separation of nonlinear mixing models

Author

Lee, Te-Won ; Koehler, Bert-Uwe ; Orglmeister, Reinhold

Author_Institution

Salk Inst., San Diego, La Jolla, CA, USA

fYear

1997

fDate

24-26 Sep 1997

Firstpage

406

Lastpage

415

Abstract

We present a new set of learning rules for the nonlinear blind source separation problem based on the information maximization criterion. The mixing model is divided into a linear mixing part and a nonlinear transfer channel. The proposed model focuses on a parametric sigmoidal nonlinearity and higher order polynomials. Our simulation results verify the convergence of the proposed algorithms

Keywords

Jacobian matrices; learning (artificial intelligence); maximum entropy methods; neural nets; polynomials; signal processing; transfer functions; blind source separation; convergence; higher order polynomials; information maximization criterion; learning rules; linear mixing; nonlinear mixing models; nonlinear transfer channel; parametric sigmoidal nonlinearity; Blind source separation; Brain modeling; Electroencephalography; Independent component analysis; Neurons; Polynomials; Signal analysis; Signal processing algorithms; Speech; Transfer functions;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks for Signal Processing [1997] VII. Proceedings of the 1997 IEEE Workshop

Conference_Location

Amelia Island, FL

ISSN

1089-3555

Print_ISBN

0-7803-4256-9

Type

conf

DOI

10.1109/NNSP.1997.622422

Filename

622422

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2400478