Two topographic maps for data visualisation

• Colin Fyfe ^[1]
  1. [1] The University of Paisley
• Localización: Data mining and knowledge discovery, ISSN 1384-5810, Vol. 14, Nº 2, 2007, págs. 207-224
• Idioma: español
• DOI: 10.1007/s10618-006-0047-5
• Texto completo no disponible (Saber más ...)
• Resumen

  • 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.

    Keywords