Visualisation of heterogeneous data with the generalised generative topographic mapping

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Abstract

Heterogeneous and incomplete datasets are common in many real-world visualisation applications. The probabilistic nature of the Generative Topographic Mapping (GTM), which was originally developed for complete continuous data, can be extended to model heterogeneous (i.e. containing both continuous and discrete values) and missing data. This paper describes and assesses the resulting model on both synthetic and real-world heterogeneous data with missing values.

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Publication date2015
Publication titleProceedings of the 6th international conference on information visualization theory and applications
EditorsJosé Braz, Andreas Kerren, Lars Linsen
PublisherSciTePress
Pages233-238
Number of pages6
ISBN (Print) 978-989-758-088-8
Original languageEnglish
Event6th International Conference on Information Visualization Theory and Applications - Berlin, Germany

Conference

Conference6th International Conference on Information Visualization Theory and Applications
Abbreviated titleIVAPP 2015
CountryGermany
CityBerlin
Period11/03/1514/03/15

    Keywords

  • data visualisation , heterogeneous and missing data, GTM, LTM

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