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, IEEE, 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
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
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