Identifiability issues in noisy ICA

IEEE Signal Processing Letters

Volumn 11, Issue 5, 2004, Pages 470-473

  Davies, Mike ^a  

^a QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

Identifiability; Independent component analysis (ICA); Noise

Indexed keywords

BLIND SOURCE SEPARATION; INDEPENDENT COMPONENT ANALYSIS; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION;

MAXIMUM LIKELIHOOD ALGORITHMS; NOISY INDEPENDENT COMPONENT ANALYSIS;

SPURIOUS SIGNAL NOISE;

EID: 2442449354     PISSN: 10709908     EISSN: None     Source Type: Journal    
DOI: 10.1109/LSP.2004.826508     Document Type: Letter

References (16)

1. H. Attias, "Independent factor analysis," Neural Comput., vol. 11, pp. 803-851, 1998.
2. M. E. Davies, "Audio source separation," in Mathematics in Signal Processing V, J. McWhirter and I. Proudler, Eds. Oxford, U.K.: Clarendon, 2002.
3. L. De Lathauwer, B. De Moor, and J. Vandewalle, "A technique for higher-order-only blind source separation," in Proc. ICONIP, Hong Kong, 1996.
4. J.-F. Cardoso, "Blind signal separation: Statistical principles," Proc. IEEE, vol. 9, pp. 2009-2025, Oct. 1998.
5. P. Comon, "Independent component analysis: A new concept," Signal Process., vol. 36, no. 3, pp. 287-314, 1994.
6. J. Eriksson and V. Koivunen, "Identifiability and separability of linear ICA models revisited," in Proc. 4th Int. Symp. Independent Component Analysis Blind Signal Separation, Nara, Japan, 2003.
7. G. H. Golub and C. F. Van Loan, Matrix Computations. Bethesda, MD: John Hopkins Univ. Press, 1989.
8. M. H. Hayes, Statistical Digital Signal Processing and Modeling. New York: Wiley, 1996.
9. A. Hyvarinen, "Independent component analysis in the presence of Gaussian noise by maximizing joint likelihood," Neurocomputing, vol. 22, pp. 49-67, 1998.
10. ____, "Gaussian moments for noisy independent component analysis," IEEE Signal Processing Lett., vol. 6, pp. 145-147, June 1999.
11. ____, "Fast ICA for noisy data using Gaussian moments," in Proc. Int. Symp. Circuits Systems, Orlando, FL, 1999.
12. A. M. Kagan, Y. V. Linnik, and C. R. Rao, Characterization Problems in Mathematical Statistics. New York: Wiley, 1973.
13. E. Moulines, J.-F. Cardoso, and E. Gassiat, "Maximum likelihood for blind signal separation and deconvolution of noisy signals using mixture models," in Proc. ICASSP, 1997.
14. C. B. Papadias, "Globally convergent blind source separation based on a multiuser kurtosis maximization criterion," IEEE Trans. Signal Processing, vol. 48, pp. 3508-3519, Dec. 2000.
15. P. Sallee and B. A. Olshausen, "Learning sparse multiscale image representations," in Proc. Conf. Advances Neural Information Processing Systems, vol. 15, 2003.
16. J. H. Wilkinson, The Algebraic Eigenvalue Problem. Oxfofd, U.K.: Clarendon, 1965.