A Growing Neural Gas Network Learns Topologies

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A Growing Neural Gas Network Learns Topologies (1995)

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by Bernd Fritzke

Venue:	Advances in Neural Information Processing Systems 7
Citations:	401 - 5 self

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@INPROCEEDINGS{Fritzke95agrowing,
    author = {Bernd Fritzke},
    title = {A Growing Neural Gas Network Learns Topologies},
    booktitle = {Advances in Neural Information Processing Systems 7},
    year = {1995},
    pages = {625--632},
    publisher = {MIT Press}
}

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Abstract

An incremental network model is introduced which is able to learn the important topological relations in a given set of input vectors by means of a simple Hebb-like learning rule. In contrast to previous approaches like the "neural gas" method of Martinetz and Schulten (1991, 1994), this model has no parameters which change over time and is able to continue learning, adding units and connections, until a performance criterion has been met. Applications of the model include vector quantization, clustering, and interpolation. 1 INTRODUCTION In unsupervised learning settings only input data is available but no information on the desired output. What can the goal of learning be in this situation? One possible objective is dimensionality reduction: finding a low-dimensional subspace of the input vector space containing most or all of the input data. Linear subspaces with this property can be computed directly by principal component analysis or iteratively with a number of network models (S...

Keyphrases

neural gas network learns topology possible objective neural gas method input vector performance criterion simple hebb-like learning rule input vector space low-dimensional subspace vector quantization incremental network model principal component analysis network model previous approach input data important topological relation unsupervised learning setting dimensionality reduction linear subspace

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