Title: Supervised, edge adaptive maps

Authors: Rolf Schatten, Nils Goerke

Addresses: Division of Neural Computation, Department of Computer Science, University of Bonn, Germany. ' Division of Neural Computation, Department of Computer Science, University of Bonn, Germany

Abstract: This paper describes a supervised, edge adaptive map. The map is defined similar to a SOM consisting of neurons connected by edges. In contradiction to a SOM it is not the positions of the neurons which are adapted, rather it is the distance between them. Thus, after the learning process the neurons represent a mapping of the input data while the neurons are soleley trained using the distances between the input data points. A supervised, edge adaptive map can be used to learn lower dimensional representations of high dimensional input data. Furthermore, it can be applied to determine the positions of landmarks when only the distances between the landmarks are known.

Keywords: supervised learning; SOM; self-organising maps; topological maps; low dimensional embeddings; automatic learning; real time; edge adaptive maps.

DOI: 10.1504/IJISTA.2007.012478

International Journal of Intelligent Systems Technologies and Applications, 2007 Vol.2 No.2/3, pp.125 - 136

Available online: 19 Feb 2007

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