Natural Gradient Approach To Blind Separation Of Over- And Under-Complete Mixtures
暂无分享,去创建一个
[1] W. Boothby. An introduction to differentiable manifolds and Riemannian geometry , 1975 .
[2] Christian Jutten,et al. Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture , 1991, Signal Process..
[3] Pierre Comon,et al. Independent component analysis, A new concept? , 1994, Signal Process..
[4] Andrzej Cichocki,et al. Robust learning algorithm for blind separation of signals , 1994 .
[5] Terrence J. Sejnowski,et al. Blind separation and blind deconvolution: an information-theoretic approach , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.
[6] Erkki Oja,et al. Signal Separation by Nonlinear Hebbian Learning , 1995 .
[7] Andrzej Cichocki,et al. A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.
[8] Jean-François Cardoso,et al. Equivariant adaptive source separation , 1996, IEEE Trans. Signal Process..
[9] Andrzej Cichocki,et al. Robust neural networks with on-line learning for blind identification and blind separation of sources , 1996 .
[10] S. Amari,et al. Maximum Likelihood Source Separation: Equivariance and Adaptivity , 1997 .
[11] Andrzej Cichocki,et al. Blind deconvolution/equalization using state-space models , 1998, Neural Networks for Signal Processing VIII. Proceedings of the 1998 IEEE Signal Processing Society Workshop (Cat. No.98TH8378).
[12] Shun-ichi Amari,et al. Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.
[13] Michael A. Saunders,et al. Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..
[14] Shun-ichi Amari,et al. Natural Gradient Learning for Over- and Under-Complete Bases in ICA , 1999, Neural Computation.