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Post-nonlinear underdetermined ICA by bayesian statistics

Lookup NU author(s): Chen Wei, Dr Li Khor, Dr Wai Lok Woo, Professor Satnam Dlay

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Abstract

The problem of nonlinear signal separation and underdetermined signal separation has received increasing attention in the research of blind signal separation. Few of them can solve the situation where nonlinear and underdetermined characteristics exist simultaneously. In this paper, a new learning algorithm based on Bayesian statistics is proposed to solve the problem of the blind separation of nonlinear and underdetermined mixtures. This paper addresses the Blind Signal Separation (BSS) of post-nonlinearly mixed signals where the number of observations is less than the number of sources. Formal derivation shows that the source signals, mixing matrix and nonlinear functions can be estimated through an iterative technique based on alternate optimization. Simulations have been carried out to demonstrate the effectiveness of the proposed algorithm in separating signals under nonlinear and underdetermined conditions. © Springer-Verlag Berlin Heidelberg 2006.


Publication metadata

Author(s): Wei C, Khor LC, Woo WL, Dlay SS

Editor(s): Rosca, J; Erdogmus, D; Principe, JC; Haykin , S

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Independent Component Analysis and Blind Signal Separation: 6th International Conference

Year of Conference: 2006

Pages: 773-780

ISSN: 0302-9743 (Print) 1611-3349 (Online)

Publisher: Springer

URL: .http:/dx.doi.org/10.1007/11679363_96

DOI: 10.1007/11679363_96

Library holdings: Search Newcastle University Library for this item

Series Title: Lecture Notes in Computer Science

ISBN: 9783540326304


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