SVD Algorithms: APEX-like versus Subspace Methods
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[1] Konstantinos Ioannis Diamantaras. Principal component learning networks and applications , 1992 .
[2] Sun-Yuan Kung,et al. A neural network learning algorithm for adaptive principal component extraction (APEX) , 1990, International Conference on Acoustics, Speech, and Signal Processing.
[3] R. Brockett. Dynamical systems that sort lists, diagonalize matrices, and solve linear programming problems , 1991 .
[4] John B. Moore,et al. Global analysis of Oja's flow for neural networks , 1994, IEEE Trans. Neural Networks.
[5] Sun-Yuan Kung,et al. Principal Component Neural Networks: Theory and Applications , 1996 .
[6] Terence D. Sanger. Two Iterative Algorithms for Computing the Singular Value Decomposition from Input/Output Samples , 1993, NIPS.
[7] Juha Karhunen,et al. Principal component neural networks — Theory and applications , 1998, Pattern Analysis and Applications.
[8] Gene H. Golub,et al. Matrix computations , 1983 .
[9] Mark D. Plumbley. Lyapunov functions for convergence of principal component algorithms , 1995, Neural Networks.
[10] Erkki Oja,et al. Neural Networks, Principal Components, and Subspaces , 1989, Int. J. Neural Syst..
[11] E. Oja. Simplified neuron model as a principal component analyzer , 1982, Journal of mathematical biology.
[12] Kurt Hornik,et al. Learning in linear neural networks: a survey , 1995, IEEE Trans. Neural Networks.
[13] Jim Kay,et al. Feature discovery under contextual supervision using mutual information , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.