Topographic Independent Component Analysis

In ordinary independent component analysis, the components are assumed to be completely independent, and they do not necessarily have any meaningful order relationships. In practice, however, the estimated independent components are often not at all independent. We propose that this residual dependence structure could be used to define a topo-graphic order for the components. In particular, a distance between two components could be defined using their higher-order correlations, and this distance could be used to create a topographic representation. Thus, we obtain a linear decomposition into approximately independent components, where the dependence of two components is approximated by the proximity of the components in the topographic representation.

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

[2]  Mark E Nelson,et al.  Brain maps and parallel computers , 1990, Trends in Neurosciences.

[3]  Richard Durbin,et al.  A dimension reduction framework for understanding cortical maps , 1990, Nature.

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

[5]  Dinh Tuan Pham,et al.  Separation of a mixture of independent sources through a maximum likelihood approach , 1992 .

[6]  E. Adelson,et al.  Directionally selective complex cells and the computation of motion energy in cat visual cortex , 1992, Vision Research.

[7]  G. Blasdel,et al.  Orientation selectivity, preference, and continuity in monkey striate cortex , 1992, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[8]  D. Heeger Normalization of cell responses in cat striate cortex , 1992, Visual Neuroscience.

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

[10]  GARY BLASDEL,et al.  Putative strategies of scene segmentation in monkey visual cortex , 1994, Neural Networks.

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

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

[13]  Andrzej Cichocki,et al.  A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.

[14]  Teuvo Kohonen,et al.  Emergence of invariant-feature detectors in the adaptive-subspace self-organizing map , 1996, Biological Cybernetics.

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

[16]  Jean-François Cardoso,et al.  Equivariant adaptive source separation , 1996, IEEE Trans. Signal Process..

[17]  Andrzej Cichocki,et al.  Robust neural networks with on-line learning for blind identification and blind separation of sources , 1996 .

[18]  N. Swindale The development of topography in the visual cortex: a review of models. , 1996, Network.

[19]  Aapo Hyvärinen,et al.  New Approximations of Differential Entropy for Independent Component Analysis and Projection Pursuit , 1997, NIPS.

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

[21]  Erkki Oja,et al.  Independent Component Analysis for Identification of Artifacts in Magnetoencephalographic Recordings , 1997, NIPS.

[22]  Erkki Oja,et al.  The nonlinear PCA learning rule in independent component analysis , 1997, Neurocomputing.

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

[24]  Erkki Oja,et al.  A class of neural networks for independent component analysis , 1997, IEEE Trans. Neural Networks.

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

[26]  Juan K. Lin Factorizing Multivariate Function Classes , 1997, NIPS.

[27]  Erkki Oja,et al.  S-Map: A Network with a Simple Self-Organization Algorithm for Generative Topographic Mappings , 1997, NIPS.

[28]  Terrence J. Sejnowski,et al.  A Unifying Objective Function for Topographic Mappings , 1997, Neural Computation.

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

[30]  J. V. van Hateren,et al.  Independent component filters of natural images compared with simple cells in primary visual cortex , 1998, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[31]  Petteri Pajunen,et al.  Blind source separation using algorithmic information theory , 1998, Neurocomputing.

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

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

[34]  M S Gray,et al.  Reliable disparity estimation through selective integration , 1998, Visual Neuroscience.

[35]  Jean-François Cardoso,et al.  Multidimensional independent component analysis , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[36]  Eero P. Simoncelli,et al.  Modeling Surround Suppression in V1 Neurons with a Statistically Derived Normalization Model , 1998, NIPS.

[37]  Bartlett W. Mel,et al.  Translation-Invariant Orientation Tuning in Visual “Complex” Cells Could Derive from Intradendritic Computations , 1998, The Journal of Neuroscience.

[38]  Klaus Obermayer,et al.  A Stochastic Self-Organizing Map for Proximity Data , 1999, Neural Computation.

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

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

[41]  Hyvarinen Sparse code shrinkage: denoising of nongaussian data by maximum likelihood estimation , 1999, Neural computation.

[42]  I. Ohzawa,et al.  Functional Micro-Organization of Primary Visual Cortex: Receptive Field Analysis of Nearby Neurons , 1999, The Journal of Neuroscience.

[43]  Aapo Hyvärinen,et al.  Sparse Code Shrinkage: Denoising of Nongaussian Data by Maximum Likelihood Estimation , 1999, Neural Computation.

[44]  Aapo Hyvärinen,et al.  Emergence of Phase- and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces , 2000, Neural Computation.

[45]  Martin A. Giese,et al.  Formation of pinwheels of preferred orientation by learning sparse neural representations of natural images , 2002, Neurocomputing.

[46]  James V. Stone Independent component analysis: an introduction , 2002, Trends in Cognitive Sciences.

[47]  A. Hyvärinen,et al.  A multi-layer sparse coding network learns contour coding from natural images , 2002, Vision Research.