title: Topographic map formation of factorized Edgeworth-expanded kernels.
abstract: 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.
authors.affiliation: K.U. Leuven, Laboratorium voor Neuro- en Psychofysiologie, Campus Gasthuisberg, Herestraat, B-3000 Leuven, Belgium. marc@neuro.kuleuven.ac.be
authors.forename: Marc M
authors.initials: MM
authors.lastname: Van Hulle
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001931:Brain Mapping; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer