Independent Component Analysis: Blind source separation by sparse decomposition in a signal dictionary

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

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

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

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

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

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

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

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

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

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

[28]  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 .

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

[30]  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).

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

[32]  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.

[33]  Shun-ichi Amari,et al.  Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.

[34]  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.

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

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

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

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

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

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

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

[42]  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.

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

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

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

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

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

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

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

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

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

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

[53]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

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

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

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

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

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

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

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

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

[62]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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