References
- J. Herault and C. Jutten, "Space or time adaptive signal processing by neural models," In Proc AIP Conf on Neural Networks for Computing, American Institute of Physics , pp. 206–211, 1986
- R. Linsker, "An application of the principle of maximum information preservation to linear systems," Advances in neural information processing systems, Morgan Kaufmann, San Francisco, CA, pp. 186–194, 1989
- A. J. Bell and T. J. Sejnowski, "An information- maximization approach to blind separation and blind deconvolution," Neural Comput, vol. 7, pp. 1129–1159, 1995 https://doi.org/10.1162/neco.1995.7.6.1129
- S. Amari, A. Cichocki, and H.H. Yang, "A new learning algorithm for blind signal separation," Advances in neural information processing systems, vol. 8, MIT Press, Cambridge, MA, 1996
- M. Zibulevsky and B.A. Pearlmutter, "Blind source separation by sparse decomposition in a signal dictionary," Neural Comput, vol. 13, pp. 863–882, 2001 https://doi.org/10.1162/089976601300014385
- F. R. Bach and M. I. Jordan, "Kernel Independent Component Analysis," Journal of Machine Learning Research, vol. 3, pp. 1-48, 2002 https://doi.org/10.1162/153244303768966085
- B. Scholkopf and A. Smola,"Learning with Kernels," (Cambridge, Mass, MIT Press, 2002)
- Michael M. Broristeiti, Alexander M. Bronstein, Michael Zibirlevshy, and Yehoshzta Y. Zeevi, "Separation of Reflections via Sparse ICA," International Journal of Imaging Systems and Technology, vol. 15, no. 1, pp. 84-91, 2005 https://doi.org/10.1002/ima.20042
- Paul D. O'Grady, Barak A. Pearlmutter, and Scott T. Rickard, "Survey of Sparse and Non-Sparse Methods in Source Separation," International Journal of Imaging Systems and Technology, vol. 15, no. 1, pp. 18-33, 2005 https://doi.org/10.1002/ima.20035
- P. Kisilev, M. Zibulevsky, and Y.Y. Zeevi. "Multiscale framework for blind source separation," JMLR, vol. 4, pp. 1339-1364, 2004 https://doi.org/10.1162/jmlr.2003.4.7-8.1339
Cited by
- Illumination normalization using independent component analysis and filtering vol.65, pp.5, 2017, https://doi.org/10.1080/13682199.2017.1338815
- Functional brain networks reconstruction using group sparsity-regularized learning 2017, https://doi.org/10.1007/s11682-017-9737-4