TCB Publications - Abstract

Thomas Martinetz and Klaus Schulten. A "neural gas" network learns topologies. In Teuvo Kohonen, Kai Mäkisara, Olli Simula, and Jari Kangas, editors, Artificial Neural Networks, pp. 397-402. Elsevier, Amsterdam, 1991.

MART91B A neural network algorithm for vector quantization of topologically arbitrarily structured manifolds of input signals is presented and applied to a data manifold M which consists of subsets of different dimensionalities. In addition to the quantization of M each neural unit i, i + 1, ..., N of the network A develops connections, described by $C_{i,j} \in $ 0,1, to those neural units j with adjacent receptive fields. The resulting connectivity matrix $C_{i,j}$ describes asymptotically the neighborhood relationships among the input data of the quantized manifold and defines a graph which reflects the often a priori unknown dimensionality and topological structure of the data manifold M.

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