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.
|Title of host publication||Proceedings of the 6th international conference on information visualization theory and applications|
|Editors||José Braz, Andreas Kerren, Lars Linsen|
|Number of pages||6|
|Publication status||Published - 2015|
- data visualisation , heterogeneous and missing data, GTM, LTM