The Block Generative Topographic Mapping

  • Rodolphe Priam
  • Mohamed Nadif
  • Gérard Govaert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5064)


This paper presents a generative model and its estimation allowing to visualize binary data. Our approach is based on the Bernoulli block mixture model and the probabilistic self-organizing maps. This leads to an efficient variant of Generative Topographic Mapping. The obtained method is parsimonious and relevant on real data.


Mixture Model Binary Data Latent Variable Model Mixture Approach Block Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Rodolphe Priam
    • 1
  • Mohamed Nadif
    • 2
  • Gérard Govaert
    • 3
  1. 1.LMA Poitiers UMR 6086Université de PoitiersFuturoscope ChasseneuilFrance
  2. 2.UFR de Mathématiques et InformatiqueUniversité Paris DescartesParisFrance
  3. 3.Heudiasyc UMR 6599UTCCompiègneFrance

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