Independent Component Analysis: ICA, graphical models and variational methods

[1]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[2]  Lucas C. Parra,et al.  On-line Convolutive Blind Source Separation of Non-Stationary Signals , 2000, J. VLSI Signal Process..

[3]  Sun-Yuan Kung,et al.  Gradient Adaptive Algorithms for Contrast-Based Blind Deconvolution , 2000, J. VLSI Signal Process..

[4]  Richard M. Everson,et al.  Inferring the eigenvalues of covariance matrices from limited, noisy data , 2000, IEEE Trans. Signal Process..

[5]  Klaus Obermayer,et al.  Blind signal separation from optical imaging recordings with extended spatial decorrelation , 2000, IEEE Transactions on Biomedical Engineering.

[6]  Te-Won Lee,et al.  Blind signal separation in teleconferencing using ICA mixture model , 2000 .

[7]  T. Sejnowski,et al.  Removing electroencephalographic artifacts by blind source separation. , 2000, Psychophysiology.

[8]  Aapo Hyvärinen,et al.  A Fast Fixed-Point Algorithm for Independent Component Analysis of Complex Valued Signals , 2000, Int. J. Neural Syst..

[9]  Terrence J. Sejnowski,et al.  Learning Overcomplete Representations , 2000, Neural Computation.

[10]  Richard M. Everson,et al.  Independent Component Analysis: A Flexible Nonlinearity and Decorrelating Manifold Approach , 1999, Neural Computation.

[11]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[12]  Christian Jutten,et al.  Source separation in post-nonlinear mixtures , 1999, IEEE Trans. Signal Process..

[13]  Bruno A. Olshausen,et al.  PROBABILISTIC FRAMEWORK FOR THE ADAPTATION AND COMPARISON OF IMAGE CODES , 1999 .

[14]  Aapo Hyvärinen,et al.  Gaussian moments for noisy independent component analysis , 1999, IEEE Signal Processing Letters.

[15]  Hagai Attias,et al.  Independent Factor Analysis , 1999, Neural Computation.

[16]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[17]  Aapo Hyvärinen,et al.  Nonlinear independent component analysis: Existence and uniqueness results , 1999, Neural Networks.

[18]  Jürgen Schmidhuber,et al.  Feature Extraction Through LOCOCODE , 1999, Neural Computation.

[19]  T. Brown,et al.  A new method for spectral decomposition using a bilinear Bayesian approach. , 1999, Journal of magnetic resonance.

[20]  Terrence J. Sejnowski,et al.  Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources , 1999, Neural Computation.

[21]  Christopher M. Bishop,et al.  Mixtures of Probabilistic Principal Component Analyzers , 1999, Neural Computation.

[22]  Zoubin Ghahramani,et al.  A Unifying Review of Linear Gaussian Models , 1999, Neural Computation.

[23]  Jean-Franois Cardoso High-Order Contrasts for Independent Component Analysis , 1999, Neural Computation.

[24]  Aapo Hyvärinen,et al.  Survey on Independent Component Analysis , 1999 .

[25]  D. Ruderman,et al.  Independent component analysis of natural image sequences yields spatio-temporal filters similar to simple cells in primary visual cortex , 1998, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[26]  Noboru Ohnishi,et al.  A method of blind separation for convolved non-stationary signals , 1998, Neurocomputing.

[27]  Mark A. Girolami,et al.  An Alternative Perspective on Adaptive Independent Component Analysis Algorithms , 1998, Neural Computation.

[28]  Andrzej Cichocki,et al.  A common neural-network model for unsupervised exploratory data analysis and independent component analysis , 1998, IEEE Trans. Neural Networks.

[29]  J. Stephen,et al.  198 New developments in source localization algorithms: Clinical examples , 1998 .

[30]  M. E. Spencer,et al.  200 Comparing the source localization accuracy of EEG and MEG for different head modeling techniques using a human skull phantom , 1998 .

[31]  Phillip A. Regalia,et al.  Acoustic echo cancellation: do IIR models offer better modeling capabilities than their FIR counterparts? , 1998, IEEE Trans. Signal Process..

[32]  L. Parra,et al.  Convolutive blind source separation based on multiple decorrelation , 1998, Neural Networks for Signal Processing VIII. Proceedings of the 1998 IEEE Signal Processing Society Workshop (Cat. No.98TH8378).

[33]  S. J. Roberts,et al.  Independent Component Analysis: Source Assessment Separation, a Bayesian Approach , 1998 .

[34]  A. Doupe,et al.  Temporal and Spectral Sensitivity of Complex Auditory Neurons in the Nucleus HVc of Male Zebra Finches , 1998, The Journal of Neuroscience.

[35]  J. H. Hateren,et al.  Independent component filters of natural images compared with simple cells in primary visual cortex , 1998 .

[36]  S Makeig,et al.  Spatially independent activity patterns in functional MRI data during the stroop color-naming task. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[37]  Erkki Oja,et al.  Independent component analysis by general nonlinear Hebbian-like learning rules , 1998, Signal Process..

[38]  Andrzej Cichocki,et al.  Information-theoretic approach to blind separation of sources in non-linear mixture , 1998, Signal Process..

[39]  Christopher M. Bishop,et al.  GTM: The Generative Topographic Mapping , 1998, Neural Computation.

[40]  S Makeig,et al.  Analysis of fMRI data by blind separation into independent spatial components , 1998, Human brain mapping.

[41]  David J. Field,et al.  Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.

[42]  Terrence J. Sejnowski,et al.  The “independent components” of natural scenes are edge filters , 1997, Vision Research.

[43]  Daniel L. Ruderman,et al.  Origins of scaling in natural images , 1996, Vision Research.

[44]  Andrzej Cichocki,et al.  Stability Analysis of Learning Algorithms for Blind Source Separation , 1997, Neural Networks.

[45]  Andrew D. Back,et al.  A First Application of Independent Component Analysis to Extracting Structure from Stock Returns , 1997, Int. J. Neural Syst..

[46]  Aapo Hyvärinen,et al.  A Fast Fixed-Point Algorithm for Independent Component Analysis , 1997, Neural Computation.

[47]  S Makeig,et al.  Blind separation of auditory event-related brain responses into independent components. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[48]  R N Vigário,et al.  Extraction of ocular artefacts from EEG using independent component analysis. , 1997, Electroencephalography and clinical neurophysiology.

[49]  Juan K. Lin,et al.  Faithful Representation of Separable Distributions , 1997, Neural Computation.

[50]  Philippe Garat,et al.  Blind separation of mixture of independent sources through a quasi-maximum likelihood approach , 1997, IEEE Trans. Signal Process..

[51]  J. Cardoso Infomax and maximum likelihood for blind source separation , 1997, IEEE Signal Processing Letters.

[52]  Eric Moulines,et al.  A blind source separation technique using second-order statistics , 1997, IEEE Trans. Signal Process..

[53]  Michael Zibulevsky,et al.  Penalty/Barrier Multiplier Methods for Convex Programming Problems , 1997, SIAM J. Optim..

[54]  P. Green,et al.  Corrigendum: On Bayesian analysis of mixtures with an unknown number of components , 1997 .

[55]  S. Shamma,et al.  Analysis of dynamic spectra in ferret primary auditory cortex. I. Characteristics of single-unit responses to moving ripple spectra. , 1996, Journal of neurophysiology.

[56]  Yoram Baram,et al.  Multidimensional density shaping by sigmoids , 1996, IEEE Trans. Neural Networks.

[57]  Paul A. Griffin,et al.  Statistical Approach to Shape from Shading: Reconstruction of Three-Dimensional Face Surfaces from Single Two-Dimensional Images , 1996, Neural Computation.

[58]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[59]  G. Kitagawa Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models , 1996 .

[60]  Michael I. Jordan,et al.  Mean Field Theory for Sigmoid Belief Networks , 1996, J. Artif. Intell. Res..

[61]  Lucas C. Parra,et al.  Statistical Independence and Novelty Detection with Information Preserving Nonlinear Maps , 1996, Neural Computation.

[62]  Istvan Pintér,et al.  Perceptual wavelet-representation of speech signals and its application to speech enhancement , 1996, Comput. Speech Lang..

[63]  R Hecht-Nielsen,et al.  Replicator neural networks for universal optimal source coding. , 1995, Science.

[64]  Christian Jutten,et al.  Blind source separation for convolutive mixtures , 1995, Signal Process..

[65]  Nathalie Delfosse,et al.  Adaptive blind separation of independent sources: A deflation approach , 1995, Signal Process..

[66]  Dirk Van Compernolle,et al.  Signal separation by symmetric adaptive decorrelation: stability, convergence, and uniqueness , 1995, IEEE Trans. Signal Process..

[67]  Gustavo Deco,et al.  Nonlinear higher-order statistical decorrelation by volume-conserving neural architectures , 1995, Neural Networks.

[68]  Kiyotoshi Matsuoka,et al.  A neural net for blind separation of nonstationary signals , 1995, Neural Networks.

[69]  L. Parra,et al.  Redundancy reduction with information-preserving nonlinear maps , 1995 .

[70]  Schuster,et al.  Separation of a mixture of independent signals using time delayed correlations. , 1994, Physical review letters.

[71]  J. Nadal,et al.  Nonlinear neurons in the low-noise limit: a factorial code maximizes information transfer Network 5 , 1994 .

[72]  Juha Karhunen,et al.  Representation and separation of signals using nonlinear PCA type learning , 1994, Neural Networks.

[73]  Terry Orlick,et al.  Positive Transitions from High-Performance Sport , 1993 .

[74]  Markus Rupp,et al.  The behavior of LMS and NLMS algorithms in the presence of spherically invariant processes , 1993, IEEE Trans. Signal Process..

[75]  A. Öztürk,et al.  Non-Gaussian random vector identification using spherically invariant random processes , 1993 .

[76]  Gilles Burel,et al.  Blind separation of sources: A nonlinear neural algorithm , 1992, Neural Networks.

[77]  Ralph Linsker,et al.  Local Synaptic Learning Rules Suffice to Maximize Mutual Information in a Linear Network , 1992, Neural Computation.

[78]  Lawrence Sirovich,et al.  Management and Analysis of Large Scientific Datasets , 1992 .

[79]  Ronald R. Coifman,et al.  Entropy-based algorithms for best basis selection , 1992, IEEE Trans. Inf. Theory.

[80]  Christian Jutten,et al.  Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture , 1991, Signal Process..

[81]  Pierre Comon,et al.  Blind separation of sources, part II: Problems statement , 1991, Signal Process..

[82]  E. Weinstein,et al.  Super-exponential methods for blind deconvolution , 1991, 17th Convention of Electrical and Electronics Engineers in Israel.

[83]  A. O'Toole,et al.  Simulating the ‘Other-race Effect* as a Problem in Perceptual Learning , 1991 .

[84]  Joseph J. Atick,et al.  Towards a Theory of Early Visual Processing , 1990, Neural Computation.

[85]  Lawrence Sirovich,et al.  Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[86]  H. Brehm,et al.  Description and generation of spherically invariant speech-model signals , 1987 .

[87]  J. Friedman Exploratory Projection Pursuit , 1987 .

[88]  Robin Sibson,et al.  What is projection pursuit , 1987 .

[89]  J. Rissanen,et al.  Modeling By Shortest Data Description* , 1978, Autom..

[90]  S. Gull,et al.  Image reconstruction from incomplete and noisy data , 1978, Nature.

[91]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[92]  Joel Goldman,et al.  Detection in the presence of spherically symmetric random vectors , 1976, IEEE Trans. Inf. Theory.

[93]  C. S. Wallace,et al.  An Information Measure for Classification , 1968, Comput. J..

[94]  A. McNair THE HALF-LIFE OF VANADIUM-50 , 1961 .

[95]  C. Mallows,et al.  Scale Mixing of Symmetric Distributions with Zero Means , 1959 .