A new concept for separability problems in blind source separation

Neural Computation

Volumn 16, Issue 9, 2004, Pages 1827-1850

  Theis, Fabian J ^a  

^a UNIVERSITY OF REGENSBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPARATIVE STUDY; COMPUTER SIMULATION; CONCEPT FORMATION; NONLINEAR SYSTEM; SIGNAL PROCESSING;

ALGORITHMS; COMPUTER SIMULATION; CONCEPT FORMATION; NEURAL NETWORKS (COMPUTER); NONLINEAR DYNAMICS; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 3142759950     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/0899766041336404     Document Type: Article

References (24)

1. Babaie-Zadeh, M., Jutten, C., & Nayebi, K. (2002). A geometric approach for separating post non-linear mixtures. In Proc. of EUSIPCO '02 (Volume 2, pp. 11-14). Toulouse, France.
2. Bauer, H. (1996). Probability theory. Berlin: Walter de Gruyter.
3. Bell, A., & Sejnowski, T. (1995). An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7:1129-1159.
4. Belouchrani, A., Meraim, K. A., Cardoso, J.-R, & Moulines, E. (1997). A blind source separation technique based on second order statistics. IEEE Transactions on Signal Processing, 45(2), 434-444.
5. Cardoso, J.-R, & Souloumiac, A. (1993). Blind beamforming for non gaussian signals. IEEE Proceedings-F, 140(6), 362-370.
6. Cichocki, A., & Amari, S. (2002). Adaptive blind signal and image processing. New York: Wiley.
7. Comon, P. (1994). Independent component analysis-a new concept? Signal Processing, 36:287-314.
8. Darmois, G. (1953). Analyse générale des liaisons stochastiques. Rev. Inst. Internationale Statist., 21, 2-8.
9. Eriksson, J., & Koivunen, V. (2003). Identifiability and separability of linear ica models revisited. In Proc. of ICA 2003, (pp. 23-27). Nara, Japan.
10. Habl, M., Bauer, C., Puntonet, C., Rodriguez-Alvarez, M., & Lang, E. (2001). Analyzing biomedical signals with probabilistic ICA and kernel-based source density estimation. In M. Sebaaly, (Ed.) Information science innovations (Proc.ISI'2001) (pp. 219-225). Alberta, Canada: ICSC Academic Press.
11. Hérault, J., & Jutten, C. (1986). Space or time adaptive signal processing by neural network models. In J. Denker (Ed.), Neural networks for computing: Proceedings of the AIP Conference (pp. 206-211). New York: American Institute of Physics.
12. Hyvärinen, A., Karhunen, J., & Oja, E. (2001). Independent component analysis. New York: Wiley.
13. Hyvärinen, A., & Oja, E. (1997). A fast fixed-point algorithm for independent component analysis. Neural Computation, 9:1483-1492.
14. Hyvärinen, A., & Pajunen, P. (1999). Nonlinear independent component analysis: Existence and uniqueness results. Neural Networks, 12(3), 429-439.
15. Kagan, A. (1986). New classes of dependent random variables and a generalization of the Darmois-Skitovitch theorem to several forms. Theory Probab. Appl., 33(2), 286-295.
16. Lee, T., & Lewicki, M. (2000). The generalized gaussian mixture model using ICA. In Proc. of ICA 2000 (pp. 239-244). Helsinki, Finland.
17. Lin, J. (1998). Factorizing multivariate function classes. In M. Kearns, M. Jordan, & S. Solla (Eds.), Advances in neural information processing systems, 10, (pp. 563-569). Cambridge, MA: MIT Press.
18. Skitovitch, V. (1953). On a property of the normal distribution. DAN SSSR, 89, 217-219.
19. Taleb, A., & Jutten, C. (1999). Source separation in post non linear mixtures. IEEE Trans. on Signal Processing, 47, 2807-2820.
20. Theis, F., & Gruber, P. (forthcoming). Separability of analytic postnonlinear blind source separation with bounded sources. In Proc. of ESANN 2004. Evere, Belgium: d-side.
21. Theis, F., Jung, A., Puntonet, C., & Lang, E. (2002). Linear geometric ICA: Fundamentals and algorithms. Neural Computation, 15, 1-21.
22. Theis, F., Puntonet, C., & Lang, E. (2003). Nonlinear geometric ICA. In Proc. of ICA 2003 (pp. 275-280). Nara, Japan.
23. Tong, L., Liu, R.-W., Soon, V., & Huang, Y.-F. (1991). Indeterminacy and identifiability of blind identification. IEEE Transactions on Circuits and Systems, 38, 499-509.
24. Yeredor, A. (2000). Blind source separation via the second characteristic function. Signal Processing, 80(5), 897-902.