Regularised nonlinear blind signal separation using sparsely connected network

IEE Proceedings: Vision, Image and Signal Processing

Volumn 152, Issue 1, 2005, Pages 61-73

  Woo, W L ^a   Dlay, S S ^a  

^a NEWCASTLE UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BLIND SOURCE SEPARATION; COMPUTATIONAL COMPLEXITY; INFORMATION THEORY; MATHEMATICAL MODELS; MATRIX ALGEBRA; MULTILAYER NEURAL NETWORKS;

NONLINEAR DEMIXER; TIKHONOV REGULARISED DEMIXING MODEL;

SIGNAL PROCESSING;

EID: 17244364404     PISSN: 1350245X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1049/ip-vis:20051190     Document Type: Article

References (23)

1. Cichocki, A., and Amari, S.: 'Adaptive blind signal and image processing learning algorithms and applications' (John Wiley, New York, 2002)
2. Hyvarinen, A., Karhunen, J., and Oja, E.: 'Independent component analysis' (John Wiley, 2001)
3. Girolami, M.: 'Advances in independent component analysis' (Springer-Verlag, UK, 2000)
4. Hyvarinen, A., and Pajunen, P.: 'Nonlinear independent component analysis: Existence and uniqueness results', Neural Netw., 1999, 8, (4), pp. 525-535
5. Jutten, C., and Karhunen, J.: 'Advances in nonlinear blind source separation'. Proc. 4th Int. Symp. on ICA and Signal Separation, Nara, Japan, 2003, pp. 245-256
6. Martinez, D., and Bray, A.: 'Nonlinear blind source separation using Kernels', IEEE Trans. Neural Netw., 2003, 14, (1), pp. 228-235
7. Harmeling, S., Ziehe, A., Blankertz, B., and Kuller, K.R.: 'Nonlinear blind source separation using kernel feature spaces'. Proc. Int. Conf. on ICA and Signal Separation, San Diego, USA, 2001, pp. 102-107
8. Tan, Y., Wang, J., and Zurada, J.: 'Nonlinear blind source separation using a radial basis function network', IEEE Trans. Neural Netw., 2001, 12, (1), pp. 124-134
9. Lin, J.K., Grier, D.G., and Cowan, J.D.: 'Source separation and density estimation by faithful equivalent SOM', Adv. Neural Inf. Process. Syst., 1997, 9
10. Babaaie-Zadeh, M., Jutten, C., and Nayebi, K.: 'Minimisation-projection approach for blind source separation in different mixing models'. Proc. 4th Int. Symp. on ICA and Signal Separation, Nara, Japan, 2003, pp. 260-264
11. Burel, G.: 'Blind separation of sources: a nonlinear neural algorithm', Neural Netw., 1992, pp. 937-947
12. Yang, H.H., Amari, S., and Cichocki, A.: 'Information-theoretic approach to blind separation of sources in nonlinear mixture', Signal Process. 1998, 64, (5), pp. 291-300
13. Taleb, A.: 'A generic framework for blind source separation in structured nonlinear models', IEEE Trans. Signal Process., 2002, 50, (8), pp. 1819-1830
14. Taleb, A., and Jutten, C.: 'Source separation in post-nonlinear mixtures', IEEE Trans. Signal Process., 1999, 47, (10), pp. 2807-2820
15. Valpola, H., Oja, E., Ilin, A., Honkela, A., and Karhunen, J.: 'Nonlinear blind source separation by variational Bayesian learning', IEICE Trans. Fundamentals Electron. Commun. Comput. Sci., 2003, E86-A, (3), pp. 532-541
16. Solazzi, M., Parisi, R., and Uncini, A.: 'Blind source separation in nonlinear mixtures by adaptive spline neural network'. Proc. Int. Conf. on ICA and Signal Separation, San Diego, USA, 2001, pp. 254-259
17. Woo, W.L., and Khor, L.C.: 'Blind restoration of nonlinearly mixed signals using multilayer polynomial neural network' IEE Proc. Vis., Image Signal Process., 2004, 151, (1), pp. 51-61
18. Woo, W.L., and Dlay, S.S.: 'Blind separation of nonlinearly mixed signals using maximum likelihood neural network', WSEAS Trans. Syst. 2003, 2, (3), pp. 675-681
19. Lee, T.W.: 'Blind source separation of nonlinear mixing models', Neural Netw., 1997, 7, pp. 121-131
20. Cybenko, G.: 'Approximation by superpositions of a sigmoidal function', Math. Control Signals Syst., 1989, 2, (4), pp. 303-314
21. Sontag, E.D.: 'Feedback stabilisation using two-hidden-layer nets', IEEE Trans. Neural Netw., 1992, 4, (3), pp. 981-990
22. Rumelhart, D., Hinton, G., and Williams, R.: 'Learning representations by backpropagation errors', Nature, 1986, (323), pp. 533-536
23. Moody, J.E., and Rognvaldsson, T.: 'Smoothing regularisers for projective basis function networks', Adv. Neural Inf. Process Syst. 1997, 9, pp. 585-591