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Regularised nonlinear blind signal separation using sparsely connected network

Lookup NU author(s): Dr Wai Lok Woo, Professor Satnam Dlay

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Abstract

A nonlinear approach based on the Tikhonov regularised cost function is presented for blind signal separation of nonlinear mixtures. The proposed approach uses a multilayer perceptron as the nonlinear demixer and combines both information theoretic learning and structural complexity learning into a single framework. It is shown that this approach can be jointly used to extract independent components while constraining the overall perceptron network to be as sparse as possible. The update algorithm for the nonlinear demixer is subsequently derived using the new cost function. Sparseness in the network connection is utilised to determine the total number of layers required in the multilayer perceptron and to prevent the nonlinear demixer from outputting arbitrary independent components. Experiments are meticulously conducted to study the performance of the new approach and the outcomes of these studies are critically assessed for performance comparison with existing methods. © IEE, 2005.


Publication metadata

Author(s): Woo WL, Dlay SS

Publication type: Article

Publication status: Published

Journal: IEE Proceedings: Vision, Image and Signal Processing

Year: 2005

Volume: 152

Issue: 1

Pages: 61-73

Print publication date: 01/02/2005

ISSN (print): 1350-245X

ISSN (electronic): 1359-7108

Publisher: The Institution of Engineering and Technology

URL: http://dx.doi.org/10.1049/ip-vis:20051190

DOI: 10.1049/ip-vis:20051190


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