Representation and separation of signals using nonlinear PCA type learning
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[1] Stephen Coombes,et al. Learning higher order correlations , 1993, Neural Networks.
[2] Adam Prügel-Bennett,et al. Unsupervised Hebbian Learning and the Shape of the Neuron Activation Function , 1993 .
[3] Sun-Yuan Kung,et al. Digital neural networks , 1993, Prentice Hall Information and System Sciences Series.
[4] Andrzej Cichocki,et al. Neural networks for optimization and signal processing , 1993 .
[5] Erkki Oja,et al. Principal components, minor components, and linear neural networks , 1992, Neural Networks.
[6] Gilles Burel,et al. Blind separation of sources: A nonlinear neural algorithm , 1992, Neural Networks.
[7] John Moody,et al. Learning rate schedules for faster stochastic gradient search , 1992, Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop.
[8] J. Karhunen,et al. Learning of sinusoidal frequencies by nonlinear constrained Hebbian algorithms , 1992, Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop.
[9] Erkki Oja,et al. Principal component analysis by homogeneous neural networks, part II: Analysis and extentions of the learning algorithm , 1992 .
[10] Ray H. White,et al. Competitive hebbian learning: Algorithm and demonstrations , 1992, Neural Networks.
[11] Tomas Hrycej. Supporting supervised learning by self-organization , 1992, Neurocomputing.
[12] Charles W. Therrien,et al. Discrete Random Signals and Statistical Signal Processing , 1992 .
[13] J. Karhunen,et al. Nonlinear Hebbian Algorithms for Sinusoidal Frequency Estimation , 1992 .
[14] Jukka Saarinen,et al. Hardware Implementations of PCA Neural Networks , 1992 .
[15] Anders Krogh,et al. Introduction to the theory of neural computation , 1994, The advanced book program.
[16] Terence D. Sanger. Optimal Hidden Units for Two-Layer nonlinear Feedforward Neural Networks , 1991, Int. J. Pattern Recognit. Artif. Intell..
[17] L. E. Russo. An outer product neural network for extracting principal components from a time series , 1991, Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop.
[18] Christian Jutten,et al. Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture , 1991, Signal Process..
[19] Pierre Comon,et al. Blind separation of sources, part II: Problems statement , 1991, Signal Process..
[20] Daniel M. Kammen,et al. Correlations in high dimensional or asymmetric data sets: Hebbian neuronal processing , 1991, Neural Networks.
[21] Juha Karhunen,et al. Tracking of sinusoidal frequencies by neural network learning algorithms , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.
[22] J. Karhunen,et al. FREQUENCY ESTIMATION BY A HEBBIAN SUBSPACE LEARNING ALGORITHM , 1991 .
[23] J. Ariel Sirat,et al. A Fast Neural Algorithm for Principal Component Analysis and Singular Value Decomposition , 1991, Int. J. Neural Syst..
[24] Erkki Oja,et al. Neural Networks, Principal Components, and Subspaces , 1989, Int. J. Neural Syst..
[25] Ralph Linsker,et al. Self-organization in a perceptual network , 1988, Computer.
[26] Steven Kay,et al. Modern Spectral Estimation: Theory and Application , 1988 .
[27] J. Karhunen. Recursive estimation of eigenvectors of correlation type matrices for signal processing applications , 1985 .
[28] Erkki Oja,et al. Subspace methods of pattern recognition , 1983 .
[29] E. Oja. Simplified neuron model as a principal component analyzer , 1982, Journal of mathematical biology.