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SEAMs: Supervised, edge adaptive maps

A Supervised, Edge Adaptive Map (SEAM) is defined similar to a Self Organizing Map (SOM), but in a SEAM the neighbourhood building edges are provided together with the input data and only the length of the edges and not the positions of the neurons are provided with input data.

Therefore, a SEAM will not learn a representation of input data points but it will learn a topological representation of the provided distances.

The adaption algorithm of a SEAM can be used to learn lower dimensional representations of high dimensional input data or it can be applied to determine the positions of landmarks while only the distances between the landmarks are known.

Reported by

Division of Neural Computation, University of Bonn
Roemerstr. 164
53117 Bonn
Germany
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