Independent component analysis by general nonlinear Hebbian-like learning rules

[1]  Ali Mansour,et al.  Blind Separation of Sources , 1999 .

[2]  Terrence J. Sejnowski,et al.  The “independent components” of natural scenes are edge filters , 1997, Vision Research.

[3]  Erkki Oja,et al.  The nonlinear PCA learning rule in independent component analysis , 1997, Neurocomputing.

[4]  A. Hyvärinen,et al.  One-unit contrast functions for independent component analysis: a statistical analysis , 1997 .

[5]  Erkki Oja,et al.  A class of neural networks for independent component analysis , 1997, IEEE Trans. Neural Networks.

[6]  Erkki Oja,et al.  Applications of neural blind separation to signal and image processing , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[7]  S. Amari,et al.  Blind equalization of switching channels by ICA and learning of learning rate , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[8]  Aapo Hyvärinen,et al.  A family of fixed-point algorithms for independent component analysis , 1997, ICASSP.

[9]  D. Chakrabarti,et al.  A fast fixed - point algorithm for independent component analysis , 1997 .

[10]  Aapo Hyvrinen.  Independent Component Analysis by Minimization of Mutual Information Independent Component Analysis by Minimization of Mutual Information Independent Component Analysis by Minimization of Mutual Information , 1997 .

[11]  Terrence J. Sejnowski,et al.  Edges are the Independent Components of Natural Scenes , 1996, NIPS.

[12]  Erkki Oja,et al.  Simple Neuron Models for Independent Component Analysis , 1996, Int. J. Neural Syst..

[13]  Erkki Oja,et al.  Robust fitting by nonlinear neural units , 1996, Neural Networks.

[14]  E. Chng.  An On-line Learning Algorithm for Blind Equalization , 1996 .

[15]  Shun-ichi Amari,et al.  Blind signal extraction using self-adaptive nonlinear Hebbian learning rule , 1996 .

[16]  Andrzej Cichocki,et al.  A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.

[17]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[18]  Nathalie Delfosse,et al.  Adaptive blind separation of independent sources: A deflation approach , 1995, Signal Process..

[19]  S. Klinke,et al.  Exploratory Projection Pursuit , 1995 .

[20]  E. Oja.  The Nonlinear PCA Learning Rule and Signal Separation - Mathematical Analysis , 1995 .

[21]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[22]  Juha Karhunen,et al.  Representation and separation of signals using nonlinear PCA type learning , 1994, Neural Networks.

[23]  Erkki Oja,et al.  Principal components, minor components, and linear neural networks , 1992, Neural Networks.

[24]  Jitendra K. Tugnait,et al.  Comments on 'New criteria for blind deconvolution of nonminimum phase systems (channels)' , 1992, IEEE Trans. Inf. Theory.

[25]  Jean-Francois Cardoso,et al.  ITERATIVE TECHNIQUES FOR BLIND SOURCE SEPARATION USING ONLY FOURTH-ORDER CUMULANTS , 1992 .

[26]  Christian Jutten,et al.  Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture , 1991, Signal Process..

[27]  Jean-Francois Cardoso,et al.  Eigen-structure of the fourth-order cumulant tensor with application to the blind source separation problem , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[28]  Terence D. Sanger,et al.  Optimal unsupervised learning in a single-layer linear feedforward neural network , 1989, Neural Networks.

[29]  E. Oja,et al.  On stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrix , 1985 .

[30]  Harold J. Kushner,et al.  wchastic. approximation methods for constrained and unconstrained systems , 1978 .