Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
High-order contrasts for independent component analysis
Neural Computation
Self-Organizing Maps
Kernel-based topographic map formation by local density modeling
Neural Computation
Joint entropy maximization in kernel-based topographic maps
Neural Computation
Kernel independent component analysis
The Journal of Machine Learning Research
Blind source separation using block-coordinate relative Newton method
Signal Processing
Edgeworth-Expanded Gaussian Mixture Density Modeling
Neural Computation
Maximum Likelihood Topographic Map Formation
Neural Computation
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Self-organizing mixture networks for probability density estimation
IEEE Transactions on Neural Networks
Self-organizing maps, vector quantization, and mixture modeling
IEEE Transactions on Neural Networks
Independent component analysis based on nonparametric density estimation
IEEE Transactions on Neural Networks
Asymptotic level density in topological feature maps
IEEE Transactions on Neural Networks
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We introduce a new learning algorithm for topographic map formation of Edgeworth-expanded Gaussian activation kernels. In order to avoid the rapid increase in kernel parameters, as the problem dimensionality increases, we factorize the kernels using a linear ICA algorithm. We apply the algorithm to a number of real-world cases, and show the advantage of the Edgeworth-expanded kernels in clustering.