S. Amari and J.-F. Cardoso. Blind source Separation-semiparametric statistical approach. IEEE Trans. Signal Processing, 45(11):2692-2700, 1997
S. Amari, A. Cichocki, and H. H. Yang. A new learning algorithm for blind signal Separation. In D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo, editors, Advances in Neural Information Processing Systems, volume 8, pages 757-763. The MITPress, 1996
H. Attias. Independent factor analysis. Neural Computation, 11:803-851, 1999
A. J. Bell and T. J. Sejnowski. An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7:1129-1159, 1995
J.-F. Cardoso. Blind signal Separation: statistical principles. Proc. IEEE, 9(10):2009-2025, Oct 1998.
P. Hall. On projection pursuit regression. Ann. Statist., 17(2):573-588, 1989
T.-W. Lee, M. Girolami, and T. Sejnowski. Independent component analysis using an extended infomax algorithm for mixed sub-Gaussian and super-Gaussian sources. Neural Computation, 11(2):409-433, 1999
G. J. McLachlan and D. Peel. Finite Mixture Models. Wiley, New York, 2000
’E. Moulines, J.-F. Cardoso, and E. Gassiat. Maximum likelihood for blind separation and deconvolution of noisy signals using mixture models. In Proc. ICASSP, pages 3617-3620, Munich, Germany, 1997
B. A. Pearlmutter and L. C. Parra. A context-sensitive generalization of ICA. In Proc. Int. Conf. on Neural Information Processing, Hong Kong, 1996
W. H. Press, S. A. Teukolsky, B. P. Flannery, and W. T. Vetterling. Numerical Recipes in C. Cambridge University Press, 2nd edition, 1992
B. W. Silverman. Kernel density estimation using the Fast Fourier Transform. Appl. Statist., 31:93-99, 1982
N. Vlassis and Y. Motomura. Efficient source adaptivity in independent component analysis. IEEE Trans. Neural Networks, 12(3), May 2001
M. P. Wand and M. C. Jones. Kernel Smoothing. Chapman & Hall, London, 1995
L. Xu, C. C. Cheung, and S. Amari. Learned parametric mixture based ICA algorithm. Neurocomputing, 22:69-80, 1998