Nonlinear independent component analysis by homomorphic transformation of the mixtures

IEEE International Conference on Neural Networks - Conference Proceedings

Volumn 1, Issue , 2004, Pages 47-52

  Erdogmus, Deniz ^a   Rao, Yadunandana N ^a   Principe, Jose C ^a  

^a University of Florida   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; LEARNING SYSTEMS; MIXTURES; PRINCIPAL COMPONENT ANALYSIS; STATISTICS; VECTORS;

MIXED SIGNALS; NONLINEAR MIXTURES; OUTPUT; REGULARIZATION;

INDEPENDENT COMPONENT ANALYSIS;

EID: 10944222196     PISSN: 10987576     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (30)

1. A. Hyvarinen, J. Karhunen, E. Oja, Independent Component Analysis, Wiley, New York, 2001.
2. A. Cichocki, S.I. Amari, Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications, Wiley, New York, 2002.
3. T.W. Lee, Independent Component Analysis: Theory and Applications, Kluwer, New York, 1998.
4. A. Ziehe, K.R. Müller, "TDSEP - An Efficient Algorithm for Blind Separation Using Time Structure," Proceedings of ICANN'98, pp. 675-680, Skovde, Sweden, 1998.
5. S. Choi, A. Cichocki, Y. Deville, "Differential Decorrelation for Nonstationary Source Separation," Proceedings of ICA'01, pp. 319-322, San Diego, California, 2001.
6. T. Bell, T. Sejnowski, "An Information-Maximization Approach to Blind Separation and Blind Deconvolution," Neural Computation, vol. 7, pp. 1129-1159, 1995.
7. K.E. Hild II, D. Erdogmus, J.C. Principe, "Blind Source Separation Using Renyi's Mutual Information," IEEE Signal Processing Letters, vol. 8, pp. 174-176, 2001.
8. A. Hyvarinen, "Fast and Robust Fixed-Point Algorithms for Independent Component Analysis," IEEE Transactions on Neural Networks, vol. 10, pp. 626-634, 1999.
9. J.F. Cardoso, A. Souloumiac, "Blind Beamforming for Non-Gaussian Signals," IEE Proceedings F: Radar and Signal Processing, vol. 140, pp. 362-370, 1993.
10. J.F. Cardoso, "Blind Signal Separation: Statistical Principles," Proceedings of IEEE, vol. 86, pp. 2009-2025, 1998.
11. J.F. Cardoso, "High-Order Contrasts for Independent Component Analysis," Neural Computation, vol. 11, pp. 157-192, 1999.
12. P. Comon, "Independent Component Analysis: A New Concept?" Signal Processing, vol. 36, pp. 287-314, 1994.
13. A. Hyvarinen, "Survey on Independent Component Analysis," Neural Computing Surveys, vol. 2, pp. 94-128, 1999.
14. A. Hyvarinen, P. Pajunen, "Nonlinear Independent Component Analysis: Existence and Uniqueness Results," Neural Networks, vol. 12, no. 3, pp. 429-139, 1999.
15. L.B. Almeida, "MISEP - Linear and Nonlinear ICA Based on Mutual Information", Journal of Machine Learning Research, vol. 4, pp. 1297-1318, 2003.
16. H. Valpola, E. Oja, A. Ilin, A. Honkela, J. Karhunen, "Nonlinear Blind Source Separation by Variational Bayesian Learning," IEICE Transactions on Fundamentals of Electronics, Communications, and Computer Sciences, vol. 86, no. 3, pp. 532-541, 2003.
17. L. Parra, "Symplectic Nonlinear Independent Component Analysis," Proceedings of NIPS'96, pp. 437-443, 1996.
18. Y. Tan, J. Wang, "Nonlinear Blind Source Separation Using Higher Order Statistics and a Genetic Algorithm," IEEE Transactions on Evolutionary Computation, vol. 5, no. 6, 2001.
19. A. Ziehe, M. Kawanabe, S. Harmeling, K.R. Muller, "Blind Separation of Post-Nonlinear Mixtures Using Linearizing Transformations and Temporal Decorrelation," Journal of Machine Learning Research, vol. 4, pp. 1319-1338, 2003.
20. S. Harmeling, A. Ziehe, M. Kawanabe, K.R. Müller, "Kernel-Based Nonlinear Blind Source Separation," Neural Computation, vol. 15, pp. 1089-1124, 2003.
21. C. Jutten, J, Karhunen, "Advances in Nonlinear Blind Source Separation," Proceedings of ICA'03, pp. 245-256, Nara, Japan, 2003.
22. rd ed., McGraw-Hill, New York, 1991.
23. S. Chen, R.A. Gopinath, "Gaussianization," Proceedings of NIPS'01, pp. 423-429, Denver, Colorado, 2001.
24. E. Parzen, "On Estimation of a Probability Density Function and Mode", in Time Series Analysis Papers, Holden-Day, San Diego, California, 1967.
25. D. Erdogmus, Information Theoretic Learning: Renyi's Entropy and its Applications to Adaptive System Training, PhD Dissertation, University of Florida, Gainesville, Florida, 2002.
26. J. Weston, A. Gammerman, M.O. Stitson, V. Vapnik, V. Vovk, C. Watkins, "Support Vector Density Estimation," in Advances in Kernel Methods: Support Vector Learning, B. Scholkopf, C.J.C. Burges, A.J. Smola (eds.), pp. 293-305, MIT Press, Cambridge, Massachusetts, 1998.
27. T. Cover, J. Thomas, Elements of Information Theory, Wiley, New York, 1991.
28. D. Erdogmus, K.E. Hild II, J.C. Principe, "Kernel Size Selection in Parzen Density Estimation," unpublished work, Nov 2003.
29. K.E. Hild II, Blind Separation of Convolutive Mixtures Using Renyi's Divergence, Ph.D. Dissertation, University of Florida, Gainesville, Florida, 2003.
30. D. Erdogmus, J.C. Principe, S.P. Kim, J.C. Sanchez, "A Recursive Renyi's Entropy Estimator," Proceedings of NNSP'02, pp. 209-217, Martigny, Switzerland, 2002.