Blind source separation using block-coordinate relative Newton method

Signal Processing

Volumn 84, Issue 8, 2004, Pages 1447-1459

  Bronstein, Alexander M ^a   Bronstein, Michael M ^a   Zibulevsky, Michael ^a  

^a TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

Author keywords

Blind source separation; Block coordinate optimization; Newton algorithm; Quasi maximum likelihood

Indexed keywords

ALGORITHMS; BLIND SOURCE SEPARATION; COMPUTATIONAL COMPLEXITY; MATHEMATICAL MODELS; MATRIX ALGEBRA; MAXIMUM LIKELIHOOD ESTIMATION; OPTIMIZATION;

BLOCK-COORDINATE OPTIMIZATION; HYPERSPECTRAL IMAGING; NEWTON ALGORITHMS; QUASI-MAXIMUM LIKELIHOOD;

SIGNAL PROCESSING;

EID: 3042852446     PISSN: 01651684     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.sigpro.2004.05.019     Document Type: Article

References (20)

1. Amari S., Cichocki A., Yang H.H. A new learning algorithm for blind signal separation, in: Advances in Neural Information Processing Systems, Vol. 8. 1996;MIT Press, Cambridge, MA.
2. Bofill P., Zibulevsky P.M. Underdetermined blind source separation using sparse representations. Signal Processing. 81(11):2001;2353-2362. Available: http://iew3.technion.ac.il/~mcib/.
3. A. Bronstein, M. Bronstein, M. Zibulevsky, Y. Zeevi, Separation of semireflective layers using sparse ICA, in: International Conference on Acoustics Speech and Signal Processing (ICASSP), Vol. 3, 2003, pp. 733-736.
4. Cardoso J.-F. High-order contrasts for independent component analysis. Neural Comput. 11(1):1999;157-192.
5. J.-F. Cardoso, JADE for real-valued data, 1999. Available: http://sig.enst.fr:80/~cardoso/guidesepsou.html .
6. Cardoso J.-F., Laheld B. Equivariant adaptive source separation. IEEE Trans. Signal Process. 44(12):1996;3017-3030.
7. Chen S.S., Donoho D.L., Saunders M.A. Atomic decomposition by basis pursuit. SIAM J. Sci. Comput. 20(1):1998;33-61.
8. Cichocki A., Unbehauen R., Rummert E. Robust learning algorithm for blind separation of signals. Electron. Lett. 30(17):1994;1386-1387.
9. Gill P.E., Murray W., Wright M.H. Practical Optimization. 1981;Academic Press, New York.
10. Grippo L., Sciandrone M. Globally convergent block-coordinate techniques for unconstrained optimization. Optim. Methods Software. 10(4):1999;587-637.
11. A. Hyvärinen, The Fast-ICA MATLAB package, 1998. Available: http://www.cis.hut.fi/~aapo .
12. Hyvärinen A. Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Networks. 10(3):1999;626-634.
13. Lewicki M.S., Olshausen B.A. A probabilistic framework for the adaptation and comparison of image codes. J. Opt. Soc. Am. A: Opt. Image Sci. Vision. 16(7):1999;1587-1601. in press.
14. S. Makeig, ICA toolbox for psychophysiological research, The Salk Institute for Biological Studies, Computational Neurobiology Laboratory, 1998. Available: http://www.cnl.salk.edu/~ica.html .
15. Olshausen B.A., Field D.J. Sparse coding with an overcomplete basis set: a strategy employed by v1? Vision Res. 37:1997;3311-3325.
16. Pham D., Garrat P. Blind separation of a mixture of independent sources through a quasi-maximum likelihood approach. IEEE Trans. Signal Process. 45(7):1997;1712-1725.
17. M. Zibulevsky, Blind source separation with relative Newton method, in: Proceedings of the Fourth International Symposium on Independent Component Analysis and Blind Signal Separation (ICA2003), 2003, pp. 897-902.
18. M. Zibulevsky, P. Kisilev, Y.Y. Zeevi, B.A. Pearlmutter, Blind source separation via multinode sparse representation, in: Advances in Neural Information Processing Systems, Vol. 12, MIT Press, Cambridge, MA, 2002.
19. Zibulevsky M., Pearlmutter B.A. Blind source separation by sparse decomposition in a signal dictionary. Neural Comput. 13(4):2001;863-882.
20. Zibulevsky M., Pearlmutter B.A., Bofill P., Kisilev P. Blind source separation by sparse decomposition. Roberts S.J., Everson R.M. Independent Components Analysis: Principles and Practice. 2001;Cambridge University Press, Cambridge.