Optimal linear compression under unreliable representation and robust PCA neural models
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Michael G. Strintzis | Kurt Hornik | Konstantinos I. Diamantaras | K. Hornik | M. Strintzis | K. Diamantaras
[1] Mark D. Plumbley. Efficient information transfer and anti-Hebbian neural networks , 1993, Neural Networks.
[2] Konstantinos I. Diamantaras. Robust hebbian learning and noisy principal component analysis , 1998, Int. J. Comput. Math..
[3] D. A. Bell,et al. Information Theory and Reliable Communication , 1969 .
[4] P. Foldiak,et al. Adaptive network for optimal linear feature extraction , 1989, International 1989 Joint Conference on Neural Networks.
[5] Michael G. Strintzis,et al. Noisy PCA theory and application in filter bank codec design , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[6] Harold J. Kushner,et al. wchastic. approximation methods for constrained and unconstrained systems , 1978 .
[7] S.Y. Kung,et al. Adaptive Principal component EXtraction (APEX) and applications , 1994, IEEE Trans. Signal Process..
[8] Kurt Hornik,et al. Learning in linear neural networks: a survey , 1995, IEEE Trans. Neural Networks.
[9] E. Capaldi,et al. The organization of behavior. , 1992, Journal of applied behavior analysis.
[10] D. Signorini,et al. Neural networks , 1995, The Lancet.
[11] H. Hotelling. Analysis of a complex of statistical variables into principal components. , 1933 .
[12] K. I. Diamantaras. Robust principal component extracting neural networks , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).
[13] E. Oja. Simplified neuron model as a principal component analyzer , 1982, Journal of mathematical biology.
[14] D. F. Morrison,et al. Multivariate Statistical Methods , 1968 .
[15] Erkki Oja,et al. Neural Networks, Principal Components, and Subspaces , 1989, Int. J. Neural Syst..
[16] Terence D. Sanger,et al. Optimal unsupervised learning in a single-layer linear feedforward neural network , 1989, Neural Networks.
[17] Tamer Basar,et al. Analysis of Recursive Stochastic Algorithms , 2001 .
[18] Ralph Linsker,et al. An Application of the Principle of Maximum Information Preservation to Linear Systems , 1988, NIPS.