Face Modeling by Information Maximization 1
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[1] T. W. Lee,et al. Chromatic structure of natural scenes. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.
[2] C. J.,et al. Maximum Likelihood and Covariant Algorithms for Independent Component Analysis , 1996 .
[3] Hanqing Lu,et al. Modeling face appearance with nonlinear independent component analysis , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..
[4] Erkki Oja,et al. Neural Networks, Principal Components, and Subspaces , 1989, Int. J. Neural Syst..
[5] Te-Won Lee,et al. Independent Component Analysis , 1998, Springer US.
[6] D. Macleod,et al. Optimal nonlinear codes for the perception of natural colours , 2001, Network.
[7] J. Nadal. Non linear neurons in the low noise limit : a factorial code maximizes information transferJean , 1994 .
[8] Ralph Linsker,et al. Self-organization in a perceptual network , 1988, Computer.
[9] Terrence J. Sejnowski,et al. An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.
[10] H. Wechsler,et al. Comparative Assessment of Independent Component Analysis (ICA) for Face Recognition , 1999 .
[11] Terrence J. Sejnowski,et al. Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Sub-Gaussian and Super-Gaussian Sources , 1999, Neural Comput..
[12] H. Sebastian Seung,et al. Learning the parts of objects by non-negative matrix factorization , 1999, Nature.
[13] Christian Jutten,et al. Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture , 1991, Signal Process..
[14] J. Austin. Associative memory , 1987 .
[15] S. Laughlin. A Simple Coding Procedure Enhances a Neuron's Information Capacity , 1981, Zeitschrift fur Naturforschung. Section C, Biosciences.
[16] A. O'Toole,et al. Structural aspects of face recognition and the other-race effect , 1994, Memory & cognition.
[17] L N Piotrowski,et al. A Demonstration of the Visual Importance and Flexibility of Spatial-Frequency Amplitude and Phase , 1982, Perception.
[18] Terrence J. Sejnowski,et al. Learning Overcomplete Representations , 2000, Neural Computation.
[19] V. Bruce,et al. Face processing: Human perception and principal components analysis , 1996, Memory & cognition.
[20] Eero P. Simoncelli. Statistical models for images: compression, restoration and synthesis , 1997, Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).
[21] E T Rolls,et al. Sparseness of the neuronal representation of stimuli in the primate temporal visual cortex. , 1995, Journal of neurophysiology.
[22] Baback Moghaddam,et al. Principal manifolds and Bayesian subspaces for visual recognition , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.
[23] Edmund T. Rolls,et al. Information encoding in the inferior temporal visual cortex: contributions of the firing rates and the correlations between the firing of neurons , 2010, Biological Cybernetics.
[24] Jian-Huang Lai,et al. Independent Component Analysis of Face Images , 2000, Biologically Motivated Computer Vision.
[25] Ming-Hsuan Yang,et al. Face Recognition Using Kernel Methods , 2001, NIPS.
[26] Marian Stewart Bartlett,et al. Image Representations for Facial Expression Coding , 1999, NIPS.
[27] Yee Whye Teh,et al. Rate-coded Restricted Boltzmann Machines for Face Recognition , 2000, NIPS.
[28] Penio S. Penev,et al. Local feature analysis: A general statistical theory for object representation , 1996 .
[29] Marian Stewart Bartlett,et al. Independent component representations for face recognition , 1998, Electronic Imaging.
[30] M. Girolami,et al. Advances in Independent Component Analysis , 2000, Perspectives in Neural Computing.
[31] Geoffrey E. Hinton,et al. Lesioning an attractor network: investigations of acquired dyslexia. , 1991, Psychological review.
[32] Thomas M. Cover,et al. Elements of Information Theory , 2005 .
[33] Barak A. Pearlmutter,et al. A Context-Sensitive Generalization of ICA , 1996 .
[34] John Porrill,et al. Undercomplete Independent Component Analysis for Signal Separation and Dimension Reduction , 1997 .
[35] David J. Field,et al. Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.
[36] Joseph J. Atick,et al. What Does the Retina Know about Natural Scenes? , 1992, Neural Computation.
[37] J. Haxby,et al. The distributed human neural system for face perception , 2000, Trends in Cognitive Sciences.
[38] Michael I. Jordan,et al. Kernel independent component analysis , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..
[39] Michael A. Webster,et al. Selective tuning of face perception , 2004 .
[40] Jongsun Kim,et al. Face Recognition Based on ICA Combined with FLD , 2002, Biometric Authentication.
[41] Pierre Comon,et al. Independent component analysis, A new concept? , 1994, Signal Process..
[42] Marian Stewart Bartlett,et al. Face image analysis by unsupervised learning , 2001 .
[43] M. Turk,et al. Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.
[44] Bruno A. Olshausen,et al. PROBABILISTIC FRAMEWORK FOR THE ADAPTATION AND COMPARISON OF IMAGE CODES , 1999 .
[45] A.V. Oppenheim,et al. The importance of phase in signals , 1980, Proceedings of the IEEE.
[46] D J Field,et al. Relations between the statistics of natural images and the response properties of cortical cells. , 1987, Journal of the Optical Society of America. A, Optics and image science.
[47] Alex Pentland,et al. View-based and modular eigenspaces for face recognition , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.
[48] D. Heeger. Normalization of cell responses in cat striate cortex , 1992, Visual Neuroscience.
[49] Andrzej Cichocki,et al. Information-theoretic approach to blind separation of sources in non-linear mixture , 1998, Signal Process..
[50] Te-Won Lee,et al. Blind source separation of nonlinear mixing models , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.
[51] Penio S. Penev. Redundancy and Dimensionality Reduction in Sparse-Distributed Representations of Natural Objects in Terms of Their Local Features , 2000, NIPS.
[52] Bruce A. Draper,et al. Recognizing faces with PCA and ICA , 2003, Comput. Vis. Image Underst..
[53] James W Tanaka,et al. An Encoding Advantage for Own-Race versus Other-Race Faces , 2003, Perception.
[54] Terrence J. Sejnowski,et al. The “independent components” of natural scenes are edge filters , 1997, Vision Research.
[55] Otto H. MacLin,et al. Figural aftereffects in the perception of faces , 1999, Psychonomic bulletin & review.
[56] Marian Stewart Bartlett,et al. Classifying Facial Actions , 1999, IEEE Trans. Pattern Anal. Mach. Intell..
[57] M. Bartlett,et al. Face image analysis by unsupervised learning and redundancy reduction , 1998 .
[58] Eero P. Simoncelli,et al. Natural image statistics and neural representation. , 2001, Annual review of neuroscience.
[59] Terrence J. Sejnowski,et al. Unsupervised Learning , 2018, Encyclopedia of GIS.
[60] Harry Wechsler,et al. The FERET database and evaluation procedure for face-recognition algorithms , 1998, Image Vis. Comput..
[61] D. Field,et al. Natural image statistics and efficient coding. , 1996, Network.
[62] Andrzej Cichocki,et al. Robust learning algorithm for blind separation of signals , 1994 .
[63] Tzyy-Ping Jung,et al. Independent Component Analysis of Electroencephalographic Data , 1995, NIPS.
[64] M. F.,et al. Bibliography , 1985, Experimental Gerontology.
[65] Andrzej Cichocki,et al. A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.
[66] David J. Field,et al. What Is the Goal of Sensory Coding? , 1994, Neural Computation.