Zhang K, Chan LW. (2005). Extended Gaussianization Method for Blind Separation of Post-Nonlinear Mixtures Neural Comput. 17

See more from authors: Zhang K · Chan LW

References and models cited by this paper

Abramowitz M, Stegun IA. (1968). Handbook of mathematical functions.

Amari S. (1998). Natural gradient works efficiently in learning Neural Comput. 10

Amari S, Cardoso JF. (1997). Blind source separation-Semi-parametric statistical approach IEEE Trans On Signal Processing. 45

Amari S, Cichocki A, Yang HH. (1998). Information theoretic approach to blind separation of sources in non-linear mixture Signal Process. 64

Amari SL, Cichocki A, Yang HH. (1996). A new learning algorithm for blind signal separation. Advances in Neural Information Processing Systems.. 8

Attias H. (1999). Independent factor analysis. Neural computation. 11 [PubMed]

Bell AJ. (2003). The co-information lattice Proc ICA2003 (Japan).

Bell AJ, Sejnowski TJ. (1995). An information-maximization approach to blind separation and blind deconvolution. Neural computation. 7 [PubMed]

Cardoso JF. (1997). Infomax and maximum likelihood for source separation IEEE Letters On Signal Processing. 4

Cardoso JF. (1998). Blind source separation: Statistical principles Proc IEEE. 86

Cardoso JF, Belouchrani A, Karim AM, Moulines E. (1997). A blind source separation technique using second-order statistics IEEE Trans Signal Processing. 45

Cardoso JF, Laheld B. (1996). Equivalent adaptive source separation. IEEE Trans Signal Proc. 44

Cardoso JF, Souloumiac A. (1993). Blind beamforming for non-gaussian signals Proc IEEE. 140

Chen SS, Gopinath RA. (2001). Gaussianization Advances in neural information processing systems. 13

Comon P. (1994). Independent component analysis, a new concept? Signal Processing. 36

Cover TM, Thomas JA. (1991). Elements of Information Theory.

Fisher RA, Cornish EA. (1937). Moments and cumulants in the specification of distributions Review Of The International Statistical Institute. 5

Friedman J, Breiman L. (1985). Estimating optimal transformations for multiple regression and correlation J Am Stat Assoc. 80

Gill PE, Murray W, Wright MH. (1981). Practical optimization.

Hornik K, White H, Stinchcombe M. (1989). Multilayer feedforward networks are universal approximators Neural Networks. 2

Hyvarinen A. (1999). Survey on Independent Component Analysis Neural Computing Surveys. 2

Hyvärinen A. (1999). Fast and robust fixed-point algorithms for independent component analysis. IEEE transactions on neural networks. 10 [PubMed]

Hyvärinen A, Oja E. (2000). Independent component analysis: algorithms and applications. Neural networks : the official journal of the International Neural Network Society. 13 [PubMed]

Hyvärinen A, Pajunen P. (1999). Nonlinear independent component analysis: Existence and uniqueness results. Neural networks : the official journal of the International Neural Network Society. 12 [PubMed]

Jaschke SR. (2002). The Cornish-Fisher expansion in the context of delta-gamma-normal approximations Journal Of Risk. 4

Johnson NL, Kotz S. (1970). Continuous univariate distributions. 1

Jutten C, Babaie-zadeh M, Pham DT, Sol-casals J. (2003). Improving algorithm speed in PNL mixture separation and Wiener system inversion Proc ICA2003.

Jutten C, Herault J. (1991). Blind separation of sources. Part I: An adaptive algorithm based on neuromimetic architecture. Signal Processing. 24

Jutten C, Taleb A. (1997). Nonlinear source separation: The post-nonlinear mixtures Proc ESANN.

Jutten C, Taleb A. (1999). Source separation in post-nonlinear mixtures IEEE Transaction On Signal Processing. 47

Jutten C, Taleb A. (1999). Batch algorithm for source separation in post-nonlinear mixtures Proc. First Int. Workshop on Independent Component Analysis and Signal Separation (ICA99).

Jutten C, Taleb A. (2000). Source separation: From dusk till dawn 2nd International Workshop on Independent Component Analysis and Blind Signal Separation (ICA 2000).

Kawanabe M, Ziehe A, Harmeling S, Mueller KR. (2001). Separation of post-nonlinear mixtures using ACE and temporal decorrelation Proc Int Workshop On Independent Component Analysis And Blind Signal Separation.

Kawanabe M, Ziehe A, Harmeling S, Mueller KR. (2003). Blind separation of post-nonlinear mixtures using Gaussianizing transformations and temporal decorrelation Proc ICA2003.

Lee TW, Girolami M, Sejnowski TJ. (1999). Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural computation. 11 [PubMed]

Lee TW, Koehler B, Orglmeister R. (1997). Blind separation of nonlinear mixing models IEEE International Workshop on Neural Networks for Signal Processing.

Mansour A, Barros A, Ohnishi N. (2000). Blind separation of sources: Methods, assumptions and applications IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, Special Section on Digital Signal Processing in IEICE EA. E83

Neuts MF. (1995). Advanced probability theory (2nd ed).

Oja E. (1997). The nonlinear PCA learning rule and signal separation: Mathematical analysis Neurocomputing. 17

Oja E, Hyvarinen A, Karunen J. (2001). Independent component analysis.

Pham DT. (2000). Blind separation of instantaneous mixture of sources based on order statistics IEEE Trans On Signal Processing. 48

Pham DT. (2002). Mutual information approach to blind separation of stationary sources IEEE Trans Information Theory. 48

Pham DT, Garat P. (1997). Blind separation of mixture of independent sources through a quasi-maximum likelihood approach IEEE Trans On Signal Processing. 45

Xu L, Amari SI, Yang H, Cheung C. (1997). Independent component analysis by the information-theoretic approach with mixture of densities Proc. of 1997 IEEE Intl. Conf on Neural Networks (IEEE-INNS IJCNN97).

References and models that cite this paper
This website requires cookies and limited processing of your personal data in order to function. By continuing to browse or otherwise use this site, you are agreeing to this use. See our Privacy policy and how to cite and terms of use.