The Block Generative Topographic Mapping

Authors:
Rodolphe Priam;Mohamed Nadif;Gérard Govaert
Affiliations:
LMA Poitiers UMR 6086, Université de Poitiers, Futuroscope Chasseneuil, France 86962;UFR de Mathématiques et Informatique, Université Paris Descartes, Paris, France 75006;Heudiasyc UMR 6599, UTC, Compiègne, France 60205
Venue:
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
Year:
2008

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Abstract

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.