Independent component analysis in the presence of Gaussian noise by maximizing joint likelihood

Neurocomputing

Volumn 22, Issue 1-3, 1998, Pages 49-67

  Hyvärinen, Aapo ^a  

^a AALTO UNIVERSITY   (Finland)

Author keywords

Blind source separation; Competitive learning; Independent component analysis; Maximum likelihood; Neural networks

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; DATA STRUCTURES; FUNCTIONS; MAXIMUM LIKELIHOOD ESTIMATION; SPURIOUS SIGNAL NOISE; STATISTICAL METHODS;

BLIND SOURCE SEPARATIONS; COMPETITIVE LEARNING; INDEPENDENT COMPONENT ANALYSIS;

NEURAL NETWORKS;

CALCULATION; MATHEMATICAL ANALYSIS; NOISE; PRIORITY JOURNAL; REVIEW; STATISTICAL ANALYSIS; STATISTICS;

EID: 0344505705     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0925-2312(98)00049-6     Document Type: Article

References (21)

1. Bell A.J., Sejnowski T.J. An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 7:1995;1129-1159.
2. Bell A.J., Sejnowski T.J. The ''independent components" of natural scenes are edge filters. Vision Res. 37:1997;3327-3338.
3. A. Belouchrani, J.-F. Cardoso, Maximum likelihood source separation by the expectation-maximization technique: deterministic and stochastic implementation, in: Proc. NOLTA, 1995, pp. 49-53.
4. Cardoso J.-F., Hvam Laheld B. Equivariant adaptive source separation. IEEE Trans. Signal Proces. 44(12):1996;3017-3030.
5. A. Cichocki, S.C. Douglas, S.I. Amari, Robust techniques for independent component analysis with noisy data, Neurocomputing 1998, in this issue.
6. A. Cichocki, R. Unbehauen, Neural Networks for Signal Processing and Optimization, Wiley, New York, 1994.
7. Comon P. Independent component analysis - a new concept? Signal Process. 36:1994;287-314.
8. Donoho D.L., Johnstone I.M., Kerkyacharian G., Picard D. Wavelet shrinkage: asymptopia? J. Roy. Statist. Soc. Ser. B. 57:1995;301-337.
9. A. Hyvärinen, Sparse code shrinkage: denoising of non-Gaussian data by maximum likelihood estimation, Technical Report, Helsinki University of Technology, Laboratory of Computer and Information Science, 1998.
10. Hyvärinen A., Oja E. A fast fixed-point algorithm for independent component analysis. Neural Comput. 9(7):1997;1483-1492.
11. Hyvärinen A., Oja E. Independent component analysis by general nonlinear Hebbian-like learning rules. Signal Process. 64(3):1998;301-313.
12. A. Hyvärinen, P. Hoyer, E. Oja, Sparse code shrinkage for image denoising, in: Proc. IEEE Int. Joint Conf. on Neural Networks, Anchorage, Alaska, 1998, 859-864.
13. Jutten C., Herault J. Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture. Signal Process. 24:1991;1-10.
14. J. Karhunen, A. Hyvärinen, R. Vigario, J. Hurri, E. Oja, Applications of neural blind separation to signal and image processing, in: Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP'97), Munich, Germany, 1997, pp. 131-134.
15. Karhunen J., Oja E., Wang L., Vigario R., Joutsensalo J. A class of neural networks for independent component analysis. IEEE Trans. Neural Networks. 8(3):1997;486-504.
16. J. Karhunen, P. Pajunen, Blind source separation using least-squares type adaptive algorithms, in: Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP'97), Munich, Germany, 1997, pp. 3048-3051.
17. E. Moulines, J.-F. Cardoso, E. Gassiat, Maximum likelihood for blind separation and deconvolution of noisy signals using mixture models, in: Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP'97), Munich, Germany, 1997, pp. 3617-3620.
18. Oja E. The nonlinear PCA learning rule in independent component analysis. Neurocomputing. 17(1):1997;25-46.
19. Olshausen B.A., Field D.J. Natural image statistics and efficient coding. Network. 7(2):1996;333-340.
20. J.J.K. O'Ruanaidh, W.J. Fitzgerald, Numerical Bayesian Methods Applied to Signal Processing, Springer, Berlin, 1996.
21. D.-T. Pham, P. Garrat, C. Jutten, Separation of a mixture of independent sources through a maximum likelihood approach, in: Proc. EUSIPCO 1992, pp. 771-774.