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Embedded restricted Boltzmann machines for fusion of mixed data types and applications in social measurements analysis
conference contribution
posted on 2012-01-01, 00:00 authored by Truyen TranTruyen Tran, Quoc-Dinh Phung, Svetha VenkateshSvetha VenkateshAnalysis 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.
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Event
Information Fusion. Conference (15th : 2012 : Singapore)Pagination
1814 - 1821Publisher
IEEELocation
SingaporePlace of publication
Melbourne, Vic.Start date
2012-09-07End date
2012-09-12ISBN-13
9780982443859Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2012, IEEETitle of proceedings
FUSION 2012 : Proceedings of the 15th International Conference on Information FusionUsage metrics
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