Emergence of Phase- and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces
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[1] D. Hubel,et al. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.
[2] Daniel A. Pollen,et al. Visual cortical neurons as localized spatial frequency filters , 1983, IEEE Transactions on Systems, Man, and Cybernetics.
[3] Peter Földiák,et al. Learning Invariance from Transformation Sequences , 1991, Neural Comput..
[4] Dinh Tuan Pham,et al. Separation of a mixture of independent sources through a maximum likelihood approach , 1992 .
[5] D. Heeger. Normalization of cell responses in cat striate cortex , 1992, Visual Neuroscience.
[6] Pierre Comon,et al. Independent component analysis, A new concept? , 1994, Signal Process..
[7] Horace Barlow,et al. What is the computational goal of the neocortex , 1994 .
[8] David J. Field,et al. What Is the Goal of Sensory Coding? , 1994, Neural Computation.
[9] Teuvo Kohonen,et al. Emergence of invariant-feature detectors in the adaptive-subspace self-organizing map , 1996, Biological Cybernetics.
[10] David J. Field,et al. Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.
[11] Jean-François Cardoso,et al. Equivariant adaptive source separation , 1996, IEEE Trans. Signal Process..
[12] Erkki Oja,et al. A class of neural networks for independent component analysis , 1997, IEEE Trans. Neural Networks.
[13] David J. Field,et al. Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.
[14] D. Chakrabarti,et al. A fast fixed - point algorithm for independent component analysis , 1997 .
[15] Terrence J. Sejnowski,et al. The “independent components” of natural scenes are edge filters , 1997, Vision Research.
[16] 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.
[17] J. H. Hateren,et al. Independent component filters of natural images compared with simple cells in primary visual cortex , 1998 .
[18] D. Ruderman,et al. INDEPENDENT COMPONENT ANALYSIS OF NATURAL IMAGE SEQUENCES YIELDS SPATIOTEMPORAL FILTERS SIMILAR TO SIMPLE CELLS IN PRIMARY VISUAL CORTEX , 1998 .
[19] 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).
[20] 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.
[21] Bartlett W. Mel,et al. Translation-Invariant Orientation Tuning in Visual “Complex” Cells Could Derive from Intradendritic Computations , 1998, The Journal of Neuroscience.
[22] Erkki Oja,et al. Independent component analysis by general nonlinear Hebbian-like learning rules , 1998, Signal Process..
[23] Terrence J. Sejnowski,et al. Unsupervised Learning , 2018, Encyclopedia of GIS.
[24] Juha Karhunen,et al. Local Linear Independent Component Analysis Based on Clustering , 2000, Int. J. Neural Syst..
[25] Konrad P. Körding,et al. Neurons with Two Sites of Synaptic Integration Learn Invariant Representations , 2001, Neural Computation.
[26] Aapo Hyvärinen,et al. A two-layer sparse coding model learns simple and complex cell receptive fields and topography from natural images , 2001, Vision Research.
[27] Aapo Hyvärinen,et al. Topographic Independent Component Analysis , 2001, Neural Computation.