Robust techniques for independent component analysis (ICA) with noisy data
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[1] Andrzej Cichocki,et al. Bias removal technique for blind source separation with noisy measurements , 1998 .
[2] Andrzej Cichocki,et al. Neural networks for blind decorrelation of signals , 1997, IEEE Trans. Signal Process..
[3] Mark Girolami,et al. Extraction of independent signal sources using a deflationary exploratory projection pursuit network , 1997 .
[4] Colin Fyfe,et al. Stochastic ICA Contrast Maximisation Using Oja's Nonlinear PCA Algorithm , 1997, Int. J. Neural Syst..
[5] Erkki Oja,et al. The nonlinear PCA learning rule in independent component analysis , 1997, Neurocomputing.
[6] S. Amari,et al. Multichannel blind separation and deconvolution of sources with arbitrary distributions , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.
[7] O. Macchi,et al. Adaptive separation of an unknown number of sources , 1997, Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics.
[8] Eric Moulines,et al. Maximum likelihood for blind separation and deconvolution of noisy signals using mixture models , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[9] Juha Karhunen,et al. On Neural Blind Separation with Noise Suppression and Redundancy Reduction , 1997, Int. J. Neural Syst..
[10] A. Cichocki,et al. Self-adaptive independent component analysis for sub-Gaussian and super-Gaussian mixtures with an un , 1997 .
[11] S. Amari,et al. Nonlinearity and Separation Capability: Further Justiication for the Ica Algorithm with a Learned Mixture of Parametric Densities , 1997 .
[12] Jean-François Cardoso,et al. Equivariant adaptive source separation , 1996, IEEE Trans. Signal Process..
[13] Andrzej Cichocki,et al. Robust neural networks with on-line learning for blind identification and blind separation of sources , 1996 .
[14] Shun-ichi Amari,et al. Adaptive approach to blind source separation with cancellation of additive and convolutional noise , 1996, Proceedings of Third International Conference on Signal Processing (ICSP'96).
[15] Juha Karhunen,et al. Neural approaches to independent component analysis and source separation , 1996, ESANN.
[16] C. J.,et al. Maximum Likelihood and Covariant Algorithms for Independent Component Analysis , 1996 .
[17] Andrzej Cichocki,et al. A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.
[18] Terrence J. Sejnowski,et al. An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.
[19] Kiyotoshi Matsuoka,et al. A neural net for blind separation of nonstationary signals , 1995, Neural Networks.
[20] Marimuthu Palaniswami,et al. Computational Intelligence: A Dynamic System Perspective , 1995 .
[21] Shun-ichi Amari,et al. Recurrent Neural Networks For Blind Separation of Sources , 1995 .
[22] Erkki Oja,et al. Signal Separation by Nonlinear Hebbian Learning , 1995 .
[23] Andrzej Cichocki,et al. Robust learning algorithm for blind separation of signals , 1994 .
[24] Pierre Comon,et al. Independent component analysis, A new concept? , 1994, Signal Process..
[25] Andrzej Cichocki,et al. Robust estimation of principal components by using neural network learning algorithms , 1993 .
[26] Joseph J. Atick,et al. Convergent Algorithm for Sensory Receptive Field Development , 1993, Neural Computation.
[27] P. Comon. Independent Component Analysis , 1992 .