Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A fast fixed-point algorithm for independent component analysis
Neural Computation
Sparse Distributed Memory
Estimating Overcomplete Independent Component Bases for Image Windows
Journal of Mathematical Imaging and Vision
Topographic Independent Component Analysis
Neural Computation
Learning Overcomplete Representations
Neural Computation
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
A class of neural networks for independent component analysis
IEEE Transactions on Neural Networks
Self-adaptive blind source separation based on activation functions adaptation
IEEE Transactions on Neural Networks
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Topographic and overcomplete representations of natural images/videos are important problems in computational neuroscience. We propose a new method using both topographic and overcomplete representations of natural images, showing emergence of properties similar to those of complex cells in primary visual cortex (V1). This method can be considered as an extension of model in Hyvarinen et al. [Topographic independent component analysis, Neural Comput. 13 (7) (2001) 1527-1558], which uses complete topographic representation. We utilize a sparse and approximately uncorrelated decompositions and define a topographic structure on coefficients (the dot products between basis vectors and whitened observed data vectors). The overcomplete topographic basis vectors can be learned via estimation of independent component analysis (ICA) model based on the prior assumption upon basis vectors. Computer simulations are provided to show the relationship between our model and the basic properties of complex cells in V1 cortex. The learned bases are shown to have better coding efficiency than ordinary topographic ICA (TICA) bases.