Kernel feature spaces and nonlinear blind source separation

Advances in Neural Information Processing Systems

Volumn , Issue , 2002, Pages

  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

ALGORITHMS;

INTRINSIC DIMENSIONS; KERNEL-BASED LEARNING; MATHEMATICAL CONSTRUCTIONS; NONLINEAR BLIND SOURCE SEPARATION; ORTHONORMAL BASIS; SUB MANIFOLDS; THEORETICAL FRAMEWORK; TRAINING DATA;

BLIND SOURCE SEPARATION;

EID: 84898966891     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. A. Belouchrani, K. Abed Meraim, J.-F. Cardoso, and E. Moulines. A blind source separation technique based on second order statistics. IEEE Trans. on Signal Processing, 45(2):434-444, 1997.
2. G. Burel. Blind separation of sources: a nonlinear neural algorithm. Neural Networks, 5(6):937-947, 1992.
3. C.J.C. Burges. A tutorial on support vector machines for pattern recognition. Knowledge Discovery and Data Mining, 2(2):121-167, 1998.
4. J.-F. Cardoso. Blind signal separation: statistical principles. Proceedings of the IEEE, 9(10):2009-2025, 1998.
5. J.-F. Cardoso and A. Souloumiac. Jacobi angles for simultaneous diagonalization. SIAM J.Mat.Anal.Appl., 17(1):161 ff., 1996.
6. N. Cristianini and J. Shawe-Taylor. An Introduction to Support Vector Machines. Cambridge University Press, Cambridge, UK, 2000.
7. C. Fyfe and P. L. Lai. ICA using kernel canonical correlation analysis. In Proc. Int. Workshop on Independent Component Analysis and Blind Signal Separation (ICA2000), pages 279-284, Helsinki, Finland, 2000.
8. A. Hyvarinen, J. Karhunen, and E. Oja. Independent Component Analysis. Wiley, 2001.
9. T.-W. Lee, B.U. Koehler, and R. Orglmeister. Blind source separation of nonlinear mixing models. In Neural Networks for Signal Processing VII, pages 406-415. IEEE Press, 1997.
10. J. K. Lin, D. G. Grier, and J. D. Cowan. Faithful representation of separable distributions. Neural Computation, 9(6):1305-1320, 1997.
11. G. Marques and L. Almeida. Separation of nonlinear mixtures using pattern repulsion. In Proc. Int.Workshop on Independent Component Analysis and Signal Separation (ICA'99), pages 277- 282, Aussois, France, 1999.
12. K.-R. Müller, S. Mika, G. Rätsch, K. Tsuda, and B. Schölkopf. An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks, 12(2):181-201, 2001.
13. P. Pajunen, A. Hyvärinen, and J. Karhunen. Nonlinear blind source separation by selforganizing maps. In Proc. Int. Conf. on Neural Information Processing, pages 1207-1210, Hong Kong, 1996.
14. P. Pajunen and J. Karhunen. A maximum likelihood approach to nonlinear blind source separation. In Proceedings of the 1997 Int. Conf. on Artificial Neural Networks (ICANN'97), pages 541-546, Lausanne, Switzerland, 1997.
15. B. Schölkopf, S. Mika, C.J.C. Burges, P. Knirsch, K.-R. Müller, G. Rätsch, and A.J. Smola. Input space vs. feature space in kernel-based methods. IEEE Transactions on Neural Networks, 10(5):1000-1017, September 1999.
16. B. Schölkopf, A.J. Smola, and K.-R. Müller. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10:1299-1319, 1998.
17. A. Taleb and C. Jutten. Source separation in post-nonlinear mixtures. IEEE Trans. on Signal Processing, 47(10):2807-2820, 1999.
18. H. Valpola, X. Giannakopoulos, A. Honkela, and J. Karhunen. Nonlinear independent component analysis using ensemble learning: Experiments and discussion. In Proc. Int. Workshop on Independent Component Analysis and Blind Signal Separation (ICA2000), pages 351-356, Helsinki, Finland, 2000.
19. V.N. Vapnik. The nature of statistical learning theory. Springer Verlag, New York, 1995.
20. H. H. Yang, S.-I. Amari, and A. Cichocki. Information-theoretic approach to blind separation of sources in non-linear mixture. Signal Processing, 64(3):291-300, 1998.
21. A. Ziehe and K.-R. Müller. TDSEP-an efficient algorithm for blind separation using time structure. In Proc. Int. Conf. on Artificial Neural Networks (ICANN'98), pages 675-680, Skövde, Sweden, 1998.