On the stationary state of Kohonen's self-organizing sensory mapping
Biological Cybernetics
Space or time adaptive signal processing by neural network models
AIP Conference Proceedings 151 on Neural Networks for Computing
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Self-organizing maps
Faithful representation of separable distributions
Neural Computation
Information-theoretic approach to blind separation of sources in non-linear mixture
Signal Processing - Special issue on neural networks
GTM: the generative topographic mapping
Neural Computation
Independent component analysis: theory and applications
Independent component analysis: theory and applications
Kernel-based equiprobabilistic topographic map formation
Neural Computation
Faithful representations with topographic maps
Neural Networks
Faithful Representations and Topographic Maps: From Distortion- to Information-Based Self-Organization
Neural Networks for Optimization and Signal Processing
Neural Networks for Optimization and Signal Processing
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Blind source separation-semiparametric statistical approach
IEEE Transactions on Signal Processing
Yet another algorithm which can generate topography map
IEEE Transactions on Neural Networks
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Recently, a number of heuristic techniques have been devised in order to overcome some of the limitations of the Blind Source Separation (BSS) algorithms that are rooted in the theory of Independent Component Analysis (ICA). They are usually based on topographic maps and designed to separate mixtures of signals with either sub-Gaussian or super-Gaussian source densities. In the sub-Gaussian case, the coordinates of the winning neurons in the topographic map represent the estimates of the source signal amplitudes. In the super-Gaussian case, one relies on the topographic map's ability to detect the source directions in mixture space which, in turn, correspond to the column vectors of the mixing matrix in the linear case. We will introduce a new topographic map-based heuristic for super-Gaussian BSS. It relies on the tendency of the mixture samples to cluster around the source directions. We will demonstrate its performance on linear and mildly non-linear mixtures of speech signals, including the case where there are less mixtures than sources to be separated (“non-square” BSS).