Visualisation of heterogeneous data with the generalised generative topographic mapping

Michel Randrianandrasana; Shahzad Mumtaz; Ian Nabney

Visualisation of heterogeneous data with the generalised generative topographic mapping

Michel Randrianandrasana, Shahzad Mumtaz, Ian Nabney

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Chapter in a book

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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.

Original language	English
Title of host publication	Proceedings of the 6th international conference on information visualization theory and applications
Editors	José Braz, Andreas Kerren, Lars Linsen
Publisher	SciTePress
Pages	233-238
Number of pages	6
ISBN (Print)	978-989-758-088-8
Publication status	Published - 2015

Keywords

data visualisation , heterogeneous and missing data, GTM, LTM

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Professor Ian Nabney
- ian.nabneybristol.acuk
- School of Engineering Mathematics and Technology - Professor
- Engineering Faculty Office - Associate Dean
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Cite this

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title = "Visualisation of heterogeneous data with the generalised generative topographic mapping",

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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Visualisation of heterogeneous data with the generalised generative topographic mapping. / Randrianandrasana, Michel; Mumtaz, Shahzad; Nabney, Ian.
Proceedings of the 6th international conference on information visualization theory and applications. ed. / José Braz; Andreas Kerren; Lars Linsen. SciTePress, 2015. p. 233-238.

Research output: Chapter in Book/Report/Conference proceeding › Chapter in a book

TY - CHAP

T1 - Visualisation of heterogeneous data with the generalised generative topographic mapping

AU - Randrianandrasana, Michel

AU - Mumtaz, Shahzad

AU - Nabney, Ian

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

KW - data visualisation , heterogeneous and missing data, GTM, LTM

M3 - Chapter in a book

SN - 978-989-758-088-8

SP - 233

EP - 238

BT - Proceedings of the 6th international conference on information visualization theory and applications

A2 - Braz, José

A2 - Kerren, Andreas

A2 - Linsen, Lars

PB - SciTePress

ER -

Visualisation of heterogeneous data with the generalised generative topographic mapping

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Professor Ian Nabney

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