Regularised nonlinear blind signal separation using sparsely connected network

W. L. Woo*, S. S. Dlay

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)61-73
Number of pages13
JournalIEE Proceedings: Vision, Image and Signal Processing
Issue number1
Publication statusPublished - 28 Feb 2005


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