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

Truyen Tran, Dinh Q. Phung, Svetha Venkatesh

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

1 Citation (Scopus)


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 large-scale 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.

Original languageEnglish
Title of host publication15th International Conference on Information Fusion, FUSION 2012
Number of pages8
Publication statusPublished - 24 Oct 2012
Externally publishedYes
Event15th International Conference on Information Fusion, FUSION 2012 - Singapore, Singapore
Duration: 7 Sep 201212 Sep 2012

Publication series

Name15th International Conference on Information Fusion, FUSION 2012


Conference15th International Conference on Information Fusion, FUSION 2012


  • embedded restricted Boltzmann machines
  • Information fusion
  • mixed data types
  • social measurements analysis

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