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.
Van Hulle
Marc M
MM
K.U. Leuven, Laboratorium voor Neuro- en Psychofysiologie, Campus Gasthuisberg, Herestraat, B-3000 Leuven, Belgium. marc@neuro.kuleuven.ac.be
D000465:Algorithms; D001185:Artificial Intelligence; D001931:Brain Mapping; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer