Neural networks for blind separation with unknown number of sources

Neurocomputing

Volumn 24, Issue 1-3, 1999, Pages 55-93

  Cichocki, Andrzej ^a,c   Karhunen, Juha ^b   Kasprzak, Wlodzimierz ^a,c   Vigário, Ricardo ^b  

^a RIKEN BRAIN SCIENCE INSTITUTE   (Japan)

^b AALTO UNIVERSITY   (Finland)

^c WARSAW UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

Blind separation; Image processing; Neural networks; Signal reconstruction; Unsupervised learning

Indexed keywords

ADAPTIVE ALGORITHMS; ADAPTIVE FILTERING; FEATURE EXTRACTION; IMAGE ANALYSIS; LEARNING ALGORITHMS; MEDICAL IMAGING; SIGNAL RECONSTRUCTION;

BLIND SOURCE SEPARATION; UNSUPERVISED NEURAL LEARNING;

NEURAL NETWORKS;

ADAPTATION; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER SIMULATION; CYTOARCHITECTURE; DIAGNOSTIC APPROACH ROUTE; HUMAN; HUMAN TISSUE; IMAGE PROCESSING; MAGNETOENCEPHALOGRAPHY; PRIORITY JOURNAL; VALIDATION PROCESS;

EID: 0033083243     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0925-2312(98)00091-5     Document Type: Article

References (54)

1. Amari S. Natural gradient works efficiently in learning. Neural Comput. 10:1998;251-276.
2. Amari S., Chen T.-P., Cichocki A. Stability analysis of adaptive blind source separation. Neural Networks. 10:1997;1345-1351.
3. S. Amari, A. Cichocki, H. Yang, Recurrent neural networks for blind separation of sources, Proc. Int. Symp. on Nonlinear Theory and its Applications, NOLTA-95, Las Vegas, December 1995, pp. 37-42.
4. S. Amari, A. Cichocki, H. Yang, A new learning algorithm for blind signal separation, Advances in Neural Information Processing Systems 8, MIT Press, Cambridge, MA, 1996, pp. 757-763.
5. Bell A.J., Sejnowski T.J. An information maximization approach to blind separation and blind deconvolution. Neural Comput. 7:1995;1129-1159.
6. Cao X.-R., Liu R.-W. A general approach to blind source separation. IEEE Trans. Signal Process. 44:1996;562-571.
7. Cardoso J.-F. Infomax and maximum likelihood for blind source separation. IEEE Signal Process. Lett. 4:1997;112-114.
8. J.-F. Cardoso, Entropic contrasts for source separation, in: S. Haykin (Ed.), Adaptive Unsupervised Learning, Ch. 4, John Wiley, 1999 (in press).
9. J.F. Cardoso, A. Belouchrani, B. Laheld, A new composite criterion for adaptive and iterative blind source separation, Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP-94), vol. 4, 1994, 273-276.
10. Cardoso J.F., Laheld B. Equivariant adaptive source separation. IEEE Trans. on Signal Process. 44:1996;3017-3030.
11. Cichocki A., Kasprzak W. Local adaptive learning algorithms for blind separation of natural images. Neural Network World. 6(4):1996;515-523.
12. A. Cichocki, W. Kasprzak, S. Amari, Multi-layer neural networks with a local adaptive learning rule for blind separation of source signals, Proc. Int. Symp. on Nonlinear Theory and its Applications, NOLTA-95, Las Vegas, December 1995, pp. 61-65.
13. A. Cichocki, W. Kasprzak, S. Amari, Neural network approach to blind separation and enhancement of images, Signal Processing VIII, Proc. EUSIPCO-96, EURASIP/LINT Publ., Trieste, Italy, 1996, vol. I, pp. 579-582.
14. A. Cichocki, I. Sabala, S. Choi, B. Orsier, R. Szupiluk, Self adaptive independent component analysis for sub-Gaussian and super-Gaussian mixtures with unknown number of sources and additive noise, in: Proc. 1997 Int. Symp. on Nonlinear Theory and its Applications, NOLTA-97, vol. 2, Hawaii, USA, December 1997, pp. 731-734.
15. Cichocki A., Thawonmas R., Amari S. Sequential blind signal extraction in order specified by stochastic properties. Electron. Lett. 33:1997;64-65.
16. A. Cichocki, R. Unbehauen, Neural Networks for Optimization and Signal Processing, 2nd ed., New York, Wiley, 1994, pp. 461-471.
17. Cichocki A., Unbehauen R. Robust neural networks with on-line learning for blind identification and blind separation of sources. IEEE Trans. Circuits and Systems I: Fundam. Theory Appl. 43(11):1996;894-906.
18. Cichocki A., Unbehauen R., Rummert E. Robust learning algorithm for blind separation of signals. Electron. Lett. 30(17):1994;1386-1387.
19. P. Comon, Independent component analysis - a new concept? Signal Process. 36 (1994) 287-314.
20. Comon P., Jutten C., Herault J. Blind separation of sources, Part II. problem statement Signal Process. 24:1991;11-20.
21. Delfosse N., Loubaton P. Adaptive blind separation of independent sources: a deflation approach. Signal Process. 45:1995;59-83.
22. Douglas S.C., Cichocki A. Neural networks for blind decorrelation of signals. IEEE Trans. Signal Process. 45(11):1997;2829-2842.
23. Douglas S.C., Cichocki A., Amari S. Fast-convergence filtered regressor algorithms for blind equalization. Electron. Lett. 32(23):1996;2114-2115.
24. M. Girolami, C. Fyfe, Negentropy and kurtosis as projection pursuit indices provide generalized ICA algorithms, Advances in Neural Information Processing Systems, NIPS'96 Workshop, Denver, December 1996.
25. M. Girolami, C. Fyfe, Generalized independent component analysis through unsupervised learning with emergent bussgang properties, Proc. IEEE Int. Conf. on Neural Networks, ICNN'97, Houston, TX June 1997, pp. 2147-2152.
26. A. Hyvärinen, A family of fixed-point algorithms for independent component analysis, Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP'97), Munich, Germany, April 1997, pp. 3917-3920.
27. Hyvärinen A., Oja E. Simple neuron models for independent component analysis. Int. J. Neural Systems. 7:1996;671-687.
28. Hyvärinen A., Oja E. A fast fixed-point algorithm for independent component analysis. Neural Comput. 9:1997;1483-1492.
29. V. Jousmäki, R. Hari, Somatosensory evoked fields to large-area vibrotactile stimuli, Electroenceph. Clin. Neurophysiol., under revision.
30. Jutten C., Herault J. Blind separation of sources, Part I. An adaptive algorithm based on neuromimetic architecture Signal Process. 24:1991;1-20.
31. C. Jutten, H.L. Nguyen Thi, E. Dijkstra, E. Vittoz, J. Caelen, Blind separation of sources, an algorithm for separation of convolutive mixtures, Proc. Int. Workshop on High Order Statistics, Chamrousse, France, July 1991, pp. 273-276.
32. J. Karhunen, Neural approaches to independent component analysis and source separation, Proc. 4th Eur. Symp. on Artificial Neural Networks, ESANN'96, Bruges, Belgium, April 1996, pp. 249-266.
33. Karhunen J., Cichocki A., Kasprzak W., Pajunen P. On neural blind separation with noise suppression and redundancy reduction. Int. J. Neural Systems. 8:1997;219-237.
34. J. Karhunen, A. Hyvärinen, R. Vigario, J. Hurri, E. Oja, Applications of neural blind separation to signal and image processing, Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP'97, Munich, Germany, April 1997, pp. 131-134.
35. Karhunen J., Joutsensalo J. Representation and separation of signals using nonlinear PCA type learning. Neural Networks. 7(1):1994;113-127.
36. Karhunen J., Oja E., Wang L., Vigario R., Joutsensalo J. A class of neural networks for independent component analysis. IEEE Trans. Neural Networks. 8:1997;486-504.
37. J. Karhunen, P. Pajunen, Blind source separation and tracking using nonlinear PCA criterion: a least-squares approach, Proc. Int. Conf. on Neural Networks, ICNN'97, Houston, TX, June 1997, pp. 2147-2152.
38. J. Karhunen, P. Pajunen, E. Oja, The nonlinear PCA criterion in blind source separation: relations with other approaches, Neurocomputing (1998) Vol. 22, pp. 5-20.
39. W. Kasprzak, A. Cichocki, Hidden image separation from incomplete image mixtures by independent component analysis, Proc. 13th Int. Conf. on Pattern Recognition, ICPR'96, Vienna, Austria, August 1996, IEEE Computer Society Press, Silver Spring, MD, pp. 394-398.
40. Lacoume J.L., Ruiz P. Separation of independent sources from correlated inputs. IEEE Trans. Signal Process. 40:1992;3074-3078.
41. X.-T. Ling, Y.-F. Huang, R. Liu, A neural network for blind signal separation, Proc. IEEE Int. Symp. on Circuits and Systems, ISCAS-94, London, UK, 1994, pp. 69-72.
42. Matsuoka K., Ohya M., Kawamoto M. A neural net for blind separation of non-stationary signals. Neural Networks. 8:1995;411-419.
43. E. Moreau, O. Macchi, New self-adaptive algorithms for source separation based on contrast functions, Proc. IEEE Signal Process. Workshop on Higher Order Statistics, Lake Tahoe, USA, June 1993, pp. 215-219.
44. Moreau E., Macchi O. High order contrasts for self-adaptive source separation. Int. J. Adaptive Control Signal Processing. 10:1996;19-46.
45. Oja E. The nonlinear PCA learning rule and signal separation - mathematical analysis. Neurocomputing. 17:1997;25-45.
46. E. Oja, J. Karhunen, A. Hyvärinen, R. Vigario, J. Hurri, Neural independent component analysis - approaches and applications, in: S. Amari, N. Kasabov (Eds.), Brain-Like Computing and Intelligent Information Systems, Springer, Singapore, 1997, pp. 167-188.
47. F.M. Silva, L.B. Almeida, A distributed decorrelation algorithm, in: E. Gelenbe (Ed.), Neural Networks, Advances and Applications, North-Holland, Amsterdam, 1991, pp. 145-163.
48. Tong L., Liu R., Soon V.C., Huang Y.-F. Indeterminacy and identifiability of blind identification. IEEE Trans. Circuits Systems. 38:1991;499-509.
49. R. Vigário, Extraction of ocular artifacts from EEG using independent component analysis, Electroenceph. Clin. Neurophysiol. 103, 395-404.
50. R. Vigário, V. Jousmäki, M. Hämäläinen, R. Hari, E. Oja, Independent component analysis for identification of artifacts in magnetoencephalographic recordings, Neural Information Processing Systems, Eds. M. Jordan, MIT Press, Cambridge, MA, 1998, Vol. 10, pp. 229-235. NIPS'97, Denver, December 1997.
51. R. Vigário, J. Särelä, E. Oja, Independent Component Analysis in Wave Decomposition of Auditory Evoked Fields, Proc. Int. Conf. on Artificial Neural Networks, ICANN'98, Skövde, Sweden, 1998, pp. 287-292.
52. Wang L., Karhunen J. A unified neural bigradient algorithm for robust PCA and MCA. Int. J. Neural Systems. 7:1996;53-67.
53. Weinstein E., Feder M., Oppenheim A.V. Multi-channel signal separation by de-correlation. IEEE Trans. Speech and Audio Process. 1:1993;405-413.
54. Yang H., Amari S.-I. Adaptive online learning algorithms for blind separation: maximum entropy and minimum mutual information. Neural Comput. 9:1997;1457-1482.