Principal components, minor components, and linear neural networks

[1]  E. Oja,et al.  Principal component analysis by homogeneous neural networks, Part I : The weighted subspace criterion , 1992 .

[2]  Erkki Oja,et al.  Principal component analysis by homogeneous neural networks, part II: Analysis and extentions of the learning algorithm , 1992 .

[3]  Erkki Oja,et al.  Modified Hebbian learning for curve and surface fitting , 1992, Neural Networks.

[4]  Kurt Hornik,et al.  Convergence analysis of local feature extraction algorithms , 1992, Neural Networks.

[5]  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.

[6]  Didier Le Gall,et al.  MPEG: a video compression standard for multimedia applications , 1991, CACM.

[7]  Erkki Oja,et al.  Neural Nets for Dual Subspace Pattern Recognition Method , 1991, Int. J. Neural Syst..

[8]  J. Ariel Sirat,et al.  A Fast Neural Algorithm for Principal Component Analysis and Singular Value Decomposition , 1991, Int. J. Neural Syst..

[9]  Suzanna Becker,et al.  Unsupervised Learning Procedures for Neural Networks , 1991, Int. J. Neural Syst..

[10]  G. Golub,et al.  Tracking a few extreme singular values and vectors in signal processing , 1990, Proc. IEEE.

[11]  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.

[12]  J. Rubner,et al.  A Self-Organizing Network for Principal-Component Analysis , 1989 .

[13]  Erkki Oja,et al.  Neural Networks, Principal Components, and Subspaces , 1989, Int. J. Neural Syst..

[14]  Y. Chauvin,et al.  Principal component analysis by gradient descent on a constrained linear Hebbian cell , 1989, International 1989 Joint Conference on Neural Networks.

[15]  P. Foldiak,et al.  Adaptive network for optimal linear feature extraction , 1989, International 1989 Joint Conference on Neural Networks.

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

[17]  Kurt Hornik,et al.  Neural networks and principal component analysis: Learning from examples without local minima , 1989, Neural Networks.

[18]  Ralph Linsker,et al.  Self-organization in a perceptual network , 1988, Computer.

[19]  E. Oja,et al.  Projection filter, Wiener filter, and Karhunen-Loève subspaces in digital image restoration , 1986 .

[20]  J. Karhunen Recursive estimation of eigenvectors of correlation type matrices for signal processing applications , 1985 .

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

[22]  M. Simaan,et al.  IN ThE PRESENCE OF WHITE NOISE , 1985 .

[23]  Erkki Oja,et al.  Subspace methods of pattern recognition , 1983 .

[24]  E. Oja Simplified neuron model as a principal component analyzer , 1982, Journal of mathematical biology.

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

[26]  J. Pearl,et al.  Comparison of the cosine and Fourier transforms of Markov-1 signals , 1976 .

[27]  J. Hale,et al.  Ordinary Differential Equations , 2019, Fundamentals of Numerical Mathematics for Physicists and Engineers.