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.
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Priam, R., Nadif, M., Govaert, G. (2008). The Block Generative Topographic Mapping. In: Prevost, L., Marinai, S., Schwenker, F. (eds) Artificial Neural Networks in Pattern Recognition. ANNPR 2008. Lecture Notes in Computer Science(), vol 5064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69939-2_2
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DOI: https://doi.org/10.1007/978-3-540-69939-2_2
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