Topographic map formation of factorized Edgeworth-expanded kernels

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.