Principal and Independent Components in Neural Networks - Recent Developments
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Erkki Oja | Juha Karhunen | Liu-Yue Wang | Ricardo Vigrio | E. Oja | J. Karhunen | Liuyue Wang | Ricardo Vigrio
[1] John W. Tukey,et al. A Projection Pursuit Algorithm for Exploratory Data Analysis , 1974, IEEE Transactions on Computers.
[2] Juha Karhunen,et al. Adaptive algorithms for estimating eigenvectors of correlation type matrices , 1984, ICASSP.
[3] E. Oja,et al. On stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrix , 1985 .
[4] T. Kohonen,et al. The subspace learning algorithm as a formalism for pattern recognition and neural networks , 1988, IEEE 1988 International Conference on Neural Networks.
[5] Erkki Oja,et al. Neural Networks, Principal Components, and Subspaces , 1989, Int. J. Neural Syst..
[6] Y. Chauvin,et al. Principal component analysis by gradient descent on a constrained linear Hebbian cell , 1989, International 1989 Joint Conference on Neural Networks.
[7] P. Comon. Separation Of Stochastic Processes , 1989, Workshop on Higher-Order Spectral Analysis.
[8] Kurt Hornik,et al. Neural networks and principal component analysis: Learning from examples without local minima , 1989, Neural Networks.
[9] Christian Jutten,et al. Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture , 1991, Signal Process..
[10] John C. Platt,et al. Networks for the Separation of Sources that Are Superimposed and Delayed , 1991, NIPS.
[11] Charles W. Therrien,et al. Discrete Random Signals and Statistical Signal Processing , 1992 .
[12] Gilles Burel,et al. Blind separation of sources: A nonlinear neural algorithm , 1992, Neural Networks.
[13] Erkki Oja,et al. Principal components, minor components, and linear neural networks , 1992, Neural Networks.
[14] Mark D. Plumbley. A Hebbian/anti-Hebbian network which optimizes information capacity by orthonormalizing the principal subspace , 1993 .
[15] Andrzej Cichocki,et al. Neural networks for optimization and signal processing , 1993 .
[16] Mark D. Plumbley,et al. Information Theory and Neural Networks , 1993 .
[17] Eric Moreau,et al. New self-adaptative algorithms for source separation based on contrast functions , 1993, [1993 Proceedings] IEEE Signal Processing Workshop on Higher-Order Statistics.
[18] S. Hyakin,et al. Neural Networks: A Comprehensive Foundation , 1994 .
[19] J. Karhunen. Optimization criteria and nonlinear PCA neural networks , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).
[20] Jean-Francois Cardoso,et al. Adaptive Source Separation With Uniform Performance , 1994 .
[21] Juha Karhunen,et al. Representation and separation of signals using nonlinear PCA type learning , 1994, Neural Networks.
[22] Pierre Comon,et al. Independent component analysis, A new concept? , 1994, Signal Process..
[23] Mohamad H. Hassoun,et al. Nonlinear Hebbian rule: a statistical interpretation , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).
[24] J. Karhunen,et al. Neural Estimation of Basis Vectors in Independent Component Analysis , 1995 .
[25] Gustavo Deco,et al. Nonlinear higher-order statistical decorrelation by volume-conserving neural architectures , 1995, Neural Networks.
[26] S. Klinke,et al. Exploratory Projection Pursuit , 1995 .
[27] Terrence J. Sejnowski,et al. Blind separation and blind deconvolution: an information-theoretic approach , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.
[28] Juha Karhunen,et al. Generalizations of principal component analysis, optimization problems, and neural networks , 1995, Neural Networks.
[29] Kiyotoshi Matsuoka,et al. A neural net for blind separation of nonstationary signals , 1995, Neural Networks.
[30] J. Karhunen,et al. A bigradient optimization approach for robust PCA, MCA, and source separation , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.
[31] Jean-François Cardoso,et al. Equivariant adaptive source separation , 1996, IEEE Trans. Signal Process..