Geometric source separation: algorithms and applications
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[1] Fabian J. Theis,et al. Overcomplete ICA with a Geometric Algorithm , 2002, ICANN.
[2] Fabian J Theis,et al. Adaptive signal analysis of immunological data , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.
[3] Elmar Lang,et al. Simulated annealing and density estimation for the separation of sources , 2000 .
[4] Fabian J. Theis,et al. Maximum Entropy and Minimal Mutual Information in a Nonlinear Model , 2001 .
[5] M. Bartlett,et al. Face image analysis by unsupervised learning and redundancy reduction , 1998 .
[6] Christian Jutten,et al. Space or time adaptive signal processing by neural network models , 1987 .
[7] L. Breiman,et al. Variable Kernel Estimates of Multivariate Densities , 1977 .
[8] Terrence J. Sejnowski,et al. Learning Nonlinear Overcomplete Representations for Efficient Coding , 1997, NIPS.
[9] Richard M. Everson,et al. A flexible non-linearity and decorrelating manifold approach to ICA , 1998, Neural Networks for Signal Processing VIII. Proceedings of the 1998 IEEE Signal Processing Society Workshop (Cat. No.98TH8378).
[10] Fabian J. Theis,et al. Comparison of maximum entropy and minimal mutual information in a nonlinear setting , 2002, Signal Process..
[11] Fabian J. Theis,et al. Mathematics in independent component analysis , 2003, Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings..
[12] E. Lukács,et al. A Property of the Normal Distribution , 1954 .
[13] Andrzej Cichocki,et al. Adaptive blind signal and image processing , 2002 .
[14] Ana Maria Tomé. AN ITERATIVE EIGENDECOMPOSITION APPROACH TO BLIND SOURCE SEPARATION , 2001 .
[15] Fabian J. Theis,et al. Neural network signal analysis in immunology , 2003, Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings..
[16] M. Turk,et al. Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.
[17] Schuster,et al. Separation of a mixture of independent signals using time delayed correlations. , 1994, Physical review letters.
[18] Anders Krogh,et al. Introduction to the theory of neural computation , 1994, The advanced book program.
[19] Ali Mansour,et al. Blind Separation of Sources , 1999 .
[20] Teuvo Kohonen,et al. Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.
[21] Christian Jutten,et al. Source separation in post-nonlinear mixtures , 1999, IEEE Trans. Signal Process..
[22] Te-Won Lee,et al. Independent Component Analysis , 1998, Springer US.
[23] Fabian J. Theis,et al. Extending Geometric ICA to Overcomplete and High-Dimensional BSS-Problems , 2002 .
[24] Thomas Martinetz,et al. 'Neural-gas' network for vector quantization and its application to time-series prediction , 1993, IEEE Trans. Neural Networks.
[25] Antoine Souloumiac,et al. Jacobi Angles for Simultaneous Diagonalization , 1996, SIAM J. Matrix Anal. Appl..
[26] Fabian J. Theis,et al. A Theoretical Framework for Overcomplete Geometric BMMR , 2002 .
[27] Fabian J. Theis,et al. Pattern Repulsion Revisited , 2001, IWANN.
[28] Juha Karhunen,et al. Local Independent Component Analysis Using Clustering , 1999 .
[29] Michael Herrmann,et al. Perspectives and limitations of self-organizing maps , 1996 .
[30] Lang Tong,et al. Indeterminacy and identifiability of blind identification , 1991 .
[31] Jean-Francois Cardoso,et al. INDEPENDENT COMPONENT ANALYSIS OF THE COSMIC MICROWAVE BACKGROUND , 2003 .
[32] Fabian J. Theis,et al. A Geometric ICA Procedure Based on a Lattice of the Observation Space , 2003 .
[33] G. Bodenhausen,et al. Principles of nuclear magnetic resonance in one and two dimensions , 1987 .
[34] Fabian J. Theis,et al. A Generalized Eigendecomposition Approach Using Matrix Pencils to Remove Artefacts from 2D NMR Spectra , 2003, IWANN.
[35] W. Pitts,et al. A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.
[36] Fabian J. Theis,et al. Blind Source Separation of Water Artefacts in NMR Spectra using a Matrix Pencil , 2003 .
[37] Pierre Comon. Independent component analysis - a new concept? signal processing , 1994 .
[38] Christopher M. Bishop,et al. GTM: The Generative Topographic Mapping , 1998, Neural Computation.
[39] Fabian J Theis,et al. Formalization of the Two-Step Approach to Overcomplete BSS , 2002 .
[40] H. P.. Annales de l'Institut Henri Poincaré , 1931, Nature.
[41] Alberto Prieto,et al. Separation of Speech Signals for Nonlinear Mixtures , 1999, IWANN.
[42] Terrence J. Sejnowski,et al. Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources , 1999, Neural Computation.
[43] A. J. Bell,et al. INDEPENDENT COMPONENT ANALYSIS OF BIOMEDICAL SIGNALS , 2000 .
[44] D. Ruderman. The statistics of natural images , 1994 .
[45] A. Hyvarinen,et al. On existence and uniqueness of solutions in nonlinear independent component analysis , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).
[46] Christian Jutten,et al. Nonlinear source separation: the post-nonlinear mixtures , 1997, ESANN.
[47] Juha Karhunen,et al. A Maximum Likelihood Approach to Nonlinear Blind Source Separation , 1997, ICANN.
[48] Pierre Comon,et al. Blind channel identification and extraction of more sources than sensors , 1998, Optics & Photonics.
[49] M. Girolami. Negentropy and Kurtosis as Projection Pursuit Indices Provide Generalised ICA Algorithms , 1996, NIPS 1996.
[50] Carlos G. Puntonet,et al. Neural net approach for blind separation of sources based on geometric properties , 1998, Neurocomputing.
[51] Jean-François Cardoso,et al. Equivariant adaptive source separation , 1996, IEEE Trans. Signal Process..
[52] Fabian J. Theis,et al. Removing water artefacts from 2D protein NMR spectra using GEVD with congruent matrix pencils , 2003, Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings..
[53] Gilles Pagès,et al. Two or three things that we know about the Kohonen algorithm , 1994, ESANN.
[54] Ralph Linsker,et al. Local Synaptic Learning Rules Suffice to Maximize Mutual Information in a Linear Network , 1992, Neural Computation.
[55] Fabian J. Theis. Geometric ICA in overcomplete and high-dimensional settings , 2002 .
[56] Fabian J. Theis,et al. An Improved Geometric Overcomplete Blind Source Separation Algorithm , 2009, IWANN.
[57] Fabian J. Theis,et al. SOMICA and geometric ICA , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.
[58] Fabian J. Theis,et al. Generalizing Geometric ICA to Nonlinear Settings , 2009, IWANN.
[59] Shun-ichi Amari,et al. Learned parametric mixture based ICA algorithm , 1998, Neurocomputing.
[60] Fabian J. Theis,et al. A geometric algorithm for overcomplete linear ICA , 2004, Neurocomputing.
[61] Terrence J. Sejnowski,et al. An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.
[62] S. Laughlin. A Simple Coding Procedure Enhances a Neuron's Information Capacity , 1981, Zeitschrift fur Naturforschung. Section C, Biosciences.
[63] Shun-ichi Amari,et al. Adaptive Online Learning Algorithms for Blind Separation: Maximum Entropy and Minimum Mutual Information , 1997, Neural Computation.
[64] Fabian J. Theis,et al. SOMICA - an application of self-organizing maps to geometric independent component analysis , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..
[65] Andrzej Cichocki,et al. A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.
[66] H. H. Yang,et al. A stochastic natural gradient descent algorithm for blind signal separation , 1996, Neural Networks for Signal Processing VI. Proceedings of the 1996 IEEE Signal Processing Society Workshop.
[67] Fabian J. Theis,et al. A HISTOGRAM-BASED OVERCOMPLETE ICA ALGORITHM , 2003 .
[68] Karl Pearson F.R.S.. LIII. On lines and planes of closest fit to systems of points in space , 1901 .
[69] Fabian J Theis,et al. Local features in biomedical image clusters extracted with independent component analysis , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..
[70] Zhi Ding,et al. A matrix-pencil approach to blind separation of colored nonstationary signals , 2000, IEEE Trans. Signal Process..
[71] Fabian J. Theis,et al. Linear Geometric ICA: Fundamentals and Algorithms , 2003, Neural Computation.
[72] ' F.Rojas,et al. A NEW ICA METHOD BASED ON A LATTICE OF THE OBSERVATION SPACE , 2004 .
[73] J. L. Hodges,et al. The Efficiency of Some Nonparametric Competitors of the t-Test , 1956 .
[74] Juha Karhunen,et al. Local Linear Independent Component Analysis Based on Clustering , 2000, Int. J. Neural Syst..
[75] Julio Ortega Lopera,et al. Separation of sources: A geometry-based procedure for reconstruction of n-valued signals , 1995, Signal Process..
[76] Calyampudi R. Rao,et al. Characterization Problems in Mathematical Statistics , 1976 .
[77] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[78] Te-Won Lee,et al. Nonlinear approaches to Independent Component Analysis , 2000 .
[79] B. Rollins,et al. Chemokines and disease , 2001, Nature Immunology.
[80] Peter L. Bartlett,et al. Neural Network Learning - Theoretical Foundations , 1999 .
[81] Patrik O. Hoyer,et al. EXTENSIONS OF ICA AS MODELS OF NATURAL IMAGES AND VISUAL PROCESSING , 2003 .
[82] Marian Stewart Bartlett,et al. Independent components of face images : A representation for face recognition , 1997 .
[83] Henry Leung,et al. Separation of a mixture of chaotic signals , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.
[84] S. Sheather. Density Estimation , 2004 .
[85] Richard O. Duda,et al. Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.
[86] Nuno Ferreira,et al. On-line source separation of temporally correlated signals , 2002, 2002 11th European Signal Processing Conference.
[87] Ignacio Rojas,et al. A New Geometrical ICA-based Method for Blind Separation of Speech Signals , 2003, IWANN.
[88] A.M. Tome. Blind source separation using a matrix pencil , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.
[89] Fabian J. Theis,et al. FastGeo - A Histogram Based Approach to Linear Geometric ICA , 2001 .
[90] Gilles Pagès,et al. Convergence of the one-dimensional Kohonen algorithm , 1998, Advances in Applied Probability.
[91] Ali Mansour,et al. Separation of sources using simulated annealing and competitive learning , 2002, Neurocomputing.
[92] Aapo Hyvärinen,et al. Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.
[93] G. Darmois,et al. Analyse générale des liaisons stochastiques: etude particulière de l'analyse factorielle linéaire , 1953 .
[94] Shun-ichi Amari,et al. Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.
[95] Fabian J. Theis,et al. Geometric overcomplete ICA , 2002, ESANN.
[96] Fabian J. Theis,et al. An Adaptive Approach to Blind Source Separation Using a Self-Organzing Map and a Neural Gas , 2009, IWANN.
[97] Elmar Lang,et al. Adaptive-geometric methods: Application to the separation of EEG signals , 2000 .
[98] Fabian J. Theis,et al. A new geometrical method of BSS on a lattice of the space of observations , 2003 .
[99] Fabian J. Theis,et al. Nonlinear Geometric ICA , 2003 .
[100] V. Koivunen,et al. Identifiability and Separability of Linear Ica Models Revisited , 2003 .
[101] M. Cottrell,et al. Etude d'un processus d'auto-organisation , 1987 .
[102] A. Zlotnik,et al. The biology of chemokines and their receptors. , 2000, Annual review of immunology.
[103] Fabian J. Theis,et al. How to generalize geometric ICA to higher dimensions , 2002, ESANN.
[104] D. Chakrabarti,et al. A fast fixed - point algorithm for independent component analysis , 1997 .
[105] H. Ritter,et al. Convergence properties of Kohonen's topology conserving maps: fluctuations, stability, and dimension selection , 1988, Biological Cybernetics.
[106] Ralph Linsker,et al. An Application of the Principle of Maximum Information Preservation to Linear Systems , 1988, NIPS.
[107] A. Hyvärinen,et al. Nonlinear Blind Source Separation by Self-Organizing Maps , 1996 .
[108] Simon Haykin,et al. Neural networks , 1994 .