Kernel-based nonlinear blind source separation

Neural Computation

Volumn 15, Issue 5, 2003, Pages 1089-1124

  Harmeling, Stefan ^a   Ziehe, Andreas ^a   Kawanabe, Motoaki ^a   Müller, Klaus Robert ^a,b  

^a FRAUNHOFER FIRST   (Germany)

^b UNIVERSITY OF POTSDAM   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0041376445     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976603765202677     Document Type: Article

References (42)

1. Achard, S., Pham, D., & Jutten, C. (2001). Blind source separation in post nonlinear mixtures. In T.-W. Lee (Ed.), Proc. Int. Workshop on Independent Component Analysis and Blind Signal Separation (ICA2001) (pp. 295-300). San Diego, CA: University of California, San Diego.
2. Bach, F., & Jordan, M. (2001). Kernel independent component analysis (Tech. Rep. UCB/CSD-01-1166). Berkeley: University of California, Berkeley.
3. Belouchrani, A., Meraim, K. A., Cardoso, J.-F., & Moulines, E. (1997). A blind source separation technique based on second order statistics. IEEE Trans. on Signal Processing, 45(2), 434-444.
4. Burel, G. (1992). Blind separation of sources: A nonlinear neural algorithm. Neural Networks, 5(6), 937-947.
5. Burges, C. (1998). A tutorial on support vector machines for pattern recognition. Knowledge Discovery and Data Mining, 2(2), 121-167.
6. Cardoso,J.-F. (1998). Blind signal separation: Statistical principles. Proceedings of the IEEE, 9(10), 2009-2025.
7. Cardoso,J.-F. (1999). High-order contrasts for independent component analysis. Neural Computation, 11(1), 157-192.
8. Cardoso,J.-F., & Souloumiac,A. (1996). Jacobi angles for simultaneous diagonalization. SIAMJ. Mat. Anal. Appl., 17(1), 161-164.
9. Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines. Cambridge: Cambridge University Press.
10. Fine, S., & Scheinberg, K. (2001). Efficient SVM training using low-rank kernel representations. Journal of Machine Learning Research, 2, 243-264.
11. Fyfe, C., & Lai, P. (2000). ICA using kernel canonical correlation analysis. In P. Pajunen & J. Karhunen (Eds.), Proc. Int. Workshop on Independent Component Analysis and Blind Signal Separation (ICA2000) (pp. 279-284). Helsinki, Finland: Helsinki University of Technology.
12. Harmeling, S., Ziehe, A., Kawanabe, M., Blankertz, B., & Müller, K.-R. (2001). Nonlinear blind source separation using kernel feature spaces. In T.-W. Lee (Ed.), Proc. Int. Workshop on Independent Component Analysis and Blind Signal Separation (ICA2001) (pp. 102-107). San Diego, CA: University of California, San Diego.
13. Harmeling, S., Ziehe, A., Kawanabe, M., & Müller, K.-R. (2002). Kernel feature spaces and nonlinear blind source separation. In T. Dietterich, S. Becker, & Z. Ghahramani (Eds.), Advances in neural information processing systems, 14. Cambridge, MA: MIT Press.
14. Hyvarinen, A., Karhunen, J., & Oja, E. (2001). Independent component analysis. New York: Wiley.
15. Hyvärinen, A., & Pajunen, P. (1999). Nonlinear independent component analysis: Existence and uniqueness results. Neural Networks, 12(3), 429-439.
16. Hyvärinen, A., Särelä, J., & Vigário, R. (1999). Spikes and bumps: Artefacts generated by independent component analysis with insufficient sample size. In J. F. Cardoso, Ch. Jutten, & Ph. Loubaton (Eds.), Proc. Int. Workshop on Independent Component Analysis and Signal Separation (ICA'99) (pp. 425-429). Aussois, France.
17. Iline, A., Valpola, H., & Oja, E. (2001). Detecting process state changes by nonlinear blind source separation. In T.-W. Lee (Ed.), Proc. Int. Workshop on Independent Component Analysis and Blind Signal Separation (ICA2001) (pp. 704-709). San Diego, CA: University of California, San Diego.
18. Lappalainen, H., & Honkela, A. (2000). Bayesian nonlinear independent component analysis by multi-layer perceptrons. In M. Girolami (Ed.), Advances in independent component analysis (pp. 93-121). New York: Springer-Verlag.
19. Lee, T.-W., Koehler, B., & Orglmeister, R. (1997). Blind source separation of nonlinear mixing models. Neural Networks for Signal Processing VII (pp. 406-415). Piscataway, NJ: IEEE Press.
20. Lin, J. K., Grier, D. G., & Cowan, J. D. (1997). Faithful representation of separable distributions. Neural Computation, 9(6), 1305-1320.
21. Marques, G., & Almeida, L. (1999). Separation of nonlinear mixtures using pattern repulsion. In J. F. Cardoso, Ch. Jutten, & Ph. Loubaton (Eds.), Proc. Int. Workshop on Independent Component Analysis and Signal Separation (ICA'99) (pp. 277-282). Aussois, France.
22. Meinecke, F., Ziehe, A., Kawanabe, M., & Müller, K.-R. (2002). Estimating the reliability of ICA projections. InT. Dietterich,S. Becker, &Z. Ghahramani (Eds.), Advances in neural information processing systems, 14. Cambridge, MA: MIT Press.
23. Meinecke,F., Ziehe,A., Kawanabe,M., & Müller,K.-R. (in press). A resampling approach to estimate the stability of one- or multidimensional independent components. IEEE Transactions on Biomedical Engineering.
24. Mika, S. (1998). Kernel algorithms for nonlinear signal processing in feature spaces. Master's thesis, Technical University of Berlin.
25. Molgedey,L., & Schuster,H.G. (1994). Separation of a mixture of independent signals using time delayed correlations. Physical Review Letters, 72, 3634-3636.
26. Müller, K.-R., Mika, S., Rätsch, G., Tsuda, K., & Schölkopf, B. (2001). An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks, 12(2), 181-201.
27. Müller, K.-R., Vigario, R., Meinecke, F., & Ziehe, A. (in press). Blind source separation techniques for decomposing event related brain signals. Bifurcation and Chaos.
28. Pajunen, P., Hyvärinen, A., & Karhunen, J. (1996). Nonlinear blind source separation by self-organizing maps. In Proc. Int. Conf. on Neural Information Processing (pp. 1207-1210). Hong Kong: Springer-Verlag.
29. Pajunen, P., & Karhunen, J. (1997). A maximum likelihood approach to nonlinear blind source separation. In W. Gerstner, A. Germond, M. Hasler, & J.-D. Nicoud (Eds.), Proceedings of the 1997 Int. Conf. on Artificial Neural Networks (ICANN'97) (pp. 541-546). Lausanne, Switzerland: Springer-Verlag.
30. Rissanen, J. (1978). Modeling by shortest data description. Automatica, 14, 465-471.
31. Schölkopf, B., Mika, S., Burges, C., Knirsch, P., Müller, K.-R., Rätsch, G., & Smola, A. (1999). Input space vs. feature space in kernel-based methods. IEEE Transactions on Neural Networks, 10(5), 1000-1017.
32. Schölkopf, B., & Smola, A. (2002). Learning with kernels. Cambridge, MA: MIT Press.
33. Schölkopf, B., Smola, A., & Müller, K.-R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10, 1299-1319.
34. Smola, A., & Schölkopf, B. (2000). Sparse greedy matrix approximation for machine learning. In P. Langley (Ed.), Proc. ICML'00 (pp. 911-918). San Mateo, CA: Morgan Kaufmann.
35. Taleb, A., & Jutten, C. (1999). Source separation in post-nonlinear mixtures. IEEE Trans. on Signal Processing, 47(10), 2807-2820.
36. Valpola, H., Giannakopoulos, X., Honkela, A., & Karhunen, J. (2000). Nonlinear independent component analysis using ensemble learning: Experiments and discussion. In P. Pajunen & J. Karhunen (Eds.), Proc. Int. Workshop on Independent Component Analysis and Blind Signal Separation (ICA2000) (pp. 351-356). Helsinki, Finland: Helsinki University of Technology.
37. Vapnik, V. (1995). The nature of statistical learning theory. New York: Springer-Verlag.
38. Williams, C., & Seeger, M. (2000). The effect of the input density distribution on kernel-based classifiers. In Proceedings of the Seventeenth International Conference on Machine Learning. San Mateo, CA: Morgan Kaufmann.
39. Williams, C., & Seeger, M. (2001). Using the Nyström method to speed up kernel machines. In V. T. T.G. Dietrich and T. K. Leen (Eds.), Advances in neural information processing systems, 13 (pp. 682-688). Cambridge, MA: MIT Press.
40. Yang, H. H., Amari, S.-I., & Cichocki, A. (1998). Information-theoretic approach to blind separation of sources in non-linear mixture. Signal Processing, 64(3), 291-300.
41. Ziehe, A., Kawanabe, M., Harmeling, S., & Müller, K.-R. (2001). Separation of post-nonlinear mixtures using ACE and temporal decorrelation. In T.-W. Lee (Ed.), Proc. Int. Workshop on Independent Component Analysis and Blind Signal Separation (ICA2001) (pp. 433-438). San Diego, CA: University of California, San Diego.
42. Ziehe, A., & Müller, K.-R. (1998). TDSEP - an efficient algorithm for blind separation using time structure. In L. Niklasson, M. Bodén, & T. Ziemke (Eds.), Proceedings of the 8th International Conference on Artificial Neural Networks (pp. 675-680). Berlin: Springer-Verlag.