Visualisation of heterogeneous data with the generalised generative topographic mapping

Michel Randrianandrasana, Shahzad Mumtaz, Ian Nabney

Research output: Chapter in Book/Report/Conference proceedingChapter in a book

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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.
Original languageEnglish
Title of host publicationProceedings of the 6th international conference on information visualization theory and applications
EditorsJosé Braz, Andreas Kerren, Lars Linsen
Number of pages6
ISBN (Print)978-989-758-088-8
Publication statusPublished - 2015


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


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