Redundancy reduction with information-preserving nonlinear maps
暂无分享,去创建一个
[1] P J Fox,et al. THE FOUNDATIONS OF MECHANICS. , 1918, Science.
[2] Athanasios Papoulis,et al. Probability, Random Variables and Stochastic Processes , 1965 .
[3] Peter E. Hart,et al. Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.
[4] Kang Feng,et al. The symplectic methods for the computation of hamiltonian equations , 1987 .
[5] Ralph Linsker,et al. Self-organization in a perceptual network , 1988, Computer.
[6] P. Foldiak,et al. Adaptive network for optimal linear feature extraction , 1989, International 1989 Joint Conference on Neural Networks.
[7] Erkki Oja,et al. Neural Networks, Principal Components, and Subspaces , 1989, Int. J. Neural Syst..
[8] H. Saunders,et al. Probability, Random Variables and Stochastic Processes (2nd Edition) , 1989 .
[9] J. Rubner,et al. A Self-Organizing Network for Principal-Component Analysis , 1989 .
[10] Kurt Hornik,et al. Neural networks and principal component analysis: Learning from examples without local minima , 1989, Neural Networks.
[11] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[12] Joseph J. Atick,et al. Towards a Theory of Early Visual Processing , 1990, Neural Computation.
[13] Pierre Comon,et al. Blind separation of sources, part II: Problems statement , 1991, Signal Process..
[14] Esfandiar Sorouchyari,et al. Blind separation of sources, part III: Stability analysis , 1991, Signal Process..
[15] Kai Cieliebak,et al. Symplectic Geometry , 1992, New Spaces in Physics.
[16] Joseph J. Atick,et al. What Does the Retina Know about Natural Scenes? , 1992, Neural Computation.
[17] Symplectic Phase Flow Approximation for the Numerical Integration of Canonical Systems , 1992 .
[18] Hans Josef Pesch,et al. Symplectic phase flow approximation for the numerical integration of canonical systems , 1992 .
[19] Gilles Burel,et al. Blind separation of sources: A nonlinear neural algorithm , 1992, Neural Networks.
[20] Stephen Coombes,et al. Learning higher order correlations , 1993, Neural Networks.
[21] A. Norman Redlich,et al. Redundancy Reduction as a Strategy for Unsupervised Learning , 1993, Neural Computation.
[22] A. Norman Redlich,et al. Supervised Factorial Learning , 1993, Neural Computation.
[23] Juha Karhunen,et al. Representation and separation of signals using nonlinear PCA type learning , 1994, Neural Networks.
[24] Pierre Comon,et al. Independent component analysis, A new concept? , 1994, Signal Process..
[25] Gustavo Deco,et al. Nonlinear higher-order statistical decorrelation by volume-conserving neural architectures , 1995, Neural Networks.
[26] Deco,et al. Learning time series evolution by unsupervised extraction of correlations. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.