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Embedded restricted Boltzmann machines for fusion of mixed data types and applications in social measurements analysis

Tran, Truyen, Phung, Dinh Q. and Venkatesh, Svetha 2012, Embedded restricted Boltzmann machines for fusion of mixed data types and applications in social measurements analysis, in FUSION 2012 : Proceedings of the 15th International Conference on Information Fusion, International Society on Information Fusion, Melbourne, Vic., pp. 1814-1821.

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Title Embedded restricted Boltzmann machines for fusion of mixed data types and applications in social measurements analysis
Author(s) Tran, Truyen
Phung, Dinh Q.
Venkatesh, Svetha
Conference name Information Fusion. Conference (15th : 2012 : Singapore)
Conference location Singapore
Conference dates 7 -12 Sep. 2012
Title of proceedings FUSION 2012 : Proceedings of the 15th International Conference on Information Fusion
Editor(s) [Unknown]
Publication date 2012
Conference series Information Fusion Conference
Start page 1814
End page 1821
Total pages 8
Publisher International Society on Information Fusion
Place of publication Melbourne, Vic.
Keyword(s) embedded restricted Boltzmann machines
information fusion
mixed data types
social measurements analysis
Summary Analysis and fusion of social measurements is important to understand what shapes the public’s opinion and the sustainability of the global development. However, modeling data collected from social responses is challenging as the data is typically complex and heterogeneous, which might take the form of stated facts, subjective assessment, choices, preferences or any combination thereof. Model-wise, these responses are a mixture of data types including binary, categorical, multicategorical, continuous, ordinal, count and rank data. The challenge is therefore to effectively handle mixed data in the a unified fusion framework in order to perform inference and analysis. To that end, this paper introduces eRBM (Embedded Restricted Boltzmann Machine) – a probabilistic latent variable model that can represent mixed data using a layer of hidden variables transparent across different types of data. The proposed model can comfortably support largescale data analysis tasks, including distribution modelling, data completion, prediction and visualisation. We demonstrate these versatile features on several moderate and large-scale publicly available social survey datasets.
ISBN 9780982443859
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30051375

Document type: Conference Paper
Collection: Centre for Pattern Recognition and Data Analytics
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