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. Attached Files Name Description MIMEType Size Downloads 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 IEEE 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 Copyright notice ©2012, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30051375 Document type: Conference Paper Collection: Centre for Pattern Recognition and Data Analytics Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 18 Mar 2013, 09:43:32 EST Mon, 18 Mar 2013, 10:17:14 EST Tue, 07 May 2013, 17:19:15 EST Wed, 08 May 2013, 08:35:43 EST Mon, 13 May 2013, 12:30:10 EST Mon, 13 May 2013, 12:33:14 EST Tue, 14 May 2013, 08:34:06 EST Mon, 27 May 2013, 13:18:00 EST Wed, 21 Oct 2015, 14:43:18 EST Tue, 23 Feb 2016, 13:53:25 EST Thu, 03 Mar 2016, 11:15:14 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 147 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Mon, 18 Mar 2013, 09:43:32 EST

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