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. c 2006 Elsevier Ltd. All rights reserved.

• Type: Journal
• Year: 2006
• Where: NN
• Authors: Marc M. Van Hulle
• Keywords: Gaussian Activation Kernels, Linear Ica Algorithm, Neural Networks, NN 2006, Problem Dimensionality Increases