Two-stage series-based neural network approach to nonlinear independent component analysis

P. Gao*, L. C. Khor, W. L. Woo, S. S. Dlay

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Linear Independent Component Analysis (ICA) played an important role in the development of various signal processing techniques due to the inherent simplicity. However, the assumption of linear mixture is always violated in real life, which narrows down its applications. In this paper, the problem of nonlinear independent component analysis is considered. Based on a new type of nonlinear mixing model, we propose a two-stage series-based approach to recover the original source signals. The two-stage series-based algorithm offers significant advantages in terms of reduced computational complexity and better learning dynamics of the trajectory. Simulations have also been carried out to verify the efficacy of the proposed method.

Original languageEnglish
Title of host publicationISCAS 2006
Subtitle of host publication2006 IEEE International Symposium on Circuits and Systems, Proceedings
PublisherIEEE
Pages4559-4562
Number of pages4
ISBN (Print)0-7803-9389-9
DOIs
Publication statusPublished - 11 Sep 2006
EventISCAS 2006: 2006 IEEE International Symposium on Circuits and Systems - Kos, Greece
Duration: 21 May 200624 May 2006

Conference

ConferenceISCAS 2006: 2006 IEEE International Symposium on Circuits and Systems
CountryGreece
CityKos
Period21/05/0624/05/06

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