Transpose properties in the stability and performance of the classic adaptive algorithms for blind source separation and deconvolution

Signal Processing

Volumn 80, Issue 9, 2000, Pages 1807-1822

  Von Hoff, Thomas P ^a   Lindgren, Allen G ^b   Kaelin, August N ^a  

^a ETH ZURICH   (Switzerland)

^b The University of Rhode Island   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE ALGORITHMS; COMMUNICATION CHANNELS (INFORMATION THEORY); COMPUTER SIMULATION; ERROR ANALYSIS; SIGNAL RECONSTRUCTION; SPURIOUS SIGNAL NOISE;

BLIND DECONVOLUTION; BLIND SOURCE SEPARATION; MULTICHANNEL DECONVOLUTION; SINGLE CHANNEL DECONVOLUTION; SUPER GAUSSIAN SIGNALS;

SIGNAL FILTERING AND PREDICTION;

EID: 0034276033     PISSN: 01651684     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0165-1684(00)00090-6     Document Type: Article

References (15)

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(8):1997;1345-1351.
3. S. Amari, A. Cichocki, H.H. Yang, A new learning algorithm for blind signal separation, Advances in Neural Processing Systems, Vol. 8, MIT Press, Cambridge, MA, USA, 1996, pp. 757-763.
4. Bell A.J., Sejnowski T.J. An information-maximisation approach to blind separation and blind deconvolution. Neural Comput. 7:1995;1129-1159.
5. Cardoso J.-F. The invariant approach to source separation. NOLTA '95. 1995.
6. Cardoso J.-F. Blind signal separation: statistical principles. Proc. IEEE. (10):October 1998;2009-2025.
7. Cardoso J.-F., Laheld B.H. Equivariant adaptive source separation. IEEE Trans. Signal Process. 44(12):December 1996;3017-3030.
8. Comon P. Independent component analysis, a new concept? Signal Processing. 36:1994;287-314.
9. S.C. Douglas, A. Cichocki, S. Amari, Multichannel blind separation and deconvolution of sources with arbitrary distributions, Proceedings of IEEE Workshop on Neural Networks for Signal Processing, Almelia Island Plantation, FL, September 1997, pp. 436-445.
10. R.H. Lambert, Multichannel blind deconvolution: FIR matrix algebra and separation of multipath mixtures, Ph.D. Thesis, University of South California, May 1996.
11. Lee T.-W. Independent Component Analysis. 1998;Kluwer Academic Publishers, Dordrecht.
12. Lee T.-W., Girolami M., Sejnowski T.J. Independent component analysis using an extended infomax algorithm for mixed sub-gaussian and super-gaussian sources. Neural Comput. 11(2):1999;409-433.
13. T.-W. Lee, T.J. Sejnowski, Independent component analysis for sub-gaussian and super-gaussian mixtures, Fourth Joint Symposium on Neural Computation, Vol. 7, Institute for Neural Computation, 1997, pp. 132-140.
14. Papoulis A. Probability, Random Variables, and Stochastic Processes. 1991;McGraw-Hill, New York.
15. B. Pearlmutter, L. Parra, Maximum likelihood blind source separation: A context-sensitive generalization of ICA, Advances in Neural Information Processing Systems, Vol. 9, MIT Press, Cambridge, MA, USA, 1997, pp. 613-619.