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Two topographic maps for data visualisation

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Abstract

We review a new form of self-organizing map which is based on a nonlinear projection of latent points into data space, identical to that performed in the Generative Topographic Mapping (GTM) [Bishop et al. (1997) Neurl Comput 10(1): 215–234]. But whereas the GTM is an extension of a mixture of experts, our new model is an extension of a product of experts [Hinton (2000) Technical report GCNU TR 2000-004, Gatsby Computational Neuroscience Unit, University College, London]. We show visualisation results on some real data sets and compare with the GTM. We then introduce a second mapping based on harmonic averages and show that it too creates a topographic mapping of the data. We compare these mappings on real and artificial data sets.

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Authors and Affiliations

  1. Applied Computational Intelligence Research Unit, The University of Paisley, Paisley, Scotland

    Colin Fyfe

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  1. Colin Fyfe
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Correspondence to Colin Fyfe.

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Responsible editor: Soumen Chakrabarti.

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Fyfe, C. Two topographic maps for data visualisation. Data Min Knowl Disc 14, 207–224 (2007). https://doi.org/10.1007/s10618-006-0047-5

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  • DOI: https://doi.org/10.1007/s10618-006-0047-5

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