Visualisation of heterogeneous data with the generalised generative topographic mapping


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.

Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Computer Science
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Event Title: 6th International Conference on Information Visualization Theory and Applications
Event Type: Other
Event Dates: 2015-03-11 - 2015-03-14
Uncontrolled Keywords: data visualisation ,heterogeneous and missing data,GTM,LTM,Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition
ISBN: 978-989-758-088-8
Full Text Link: http://www.scit ... na616hBT3k=&t=1
http://www.ivap ...
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2015
Authors: Randrianandrasana, Michel (ORCID Profile 0000-0002-4181-1323)
Mumtaz, Shahzad
Nabney, Ian (ORCID Profile 0000-0003-1513-993X)



Version: Accepted Version

License: Creative Commons Attribution

Export / Share Citation


Additional statistics for this record