A bayesian nonparametric joint factor model for learning shared and individual subspaces from multiple data sources Gupta, Sunil Kumar, Phung, Dinh and Venkatesh, Svetha 2012, A bayesian nonparametric joint factor model for learning shared and individual subspaces from multiple data sources, in SDM 2012 : Proceedings of the 12th SIAM International Conference on Data Mining, Society for Industrial and Applied Mathemations (SIAM), Anaheim, Calif., pp. 200-212. Attached Files Name Description MIMEType Size Downloads Title A bayesian nonparametric joint factor model for learning shared and individual subspaces from multiple data sources Author(s) Gupta, Sunil Kumar orcid.org/0000-0002-3308-1930 Phung, Dinh orcid.org/0000-0002-9977-8247 Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Conference name International Conference on Data Mining (12th : 2012 : Anaheim, Calif.) Conference location Anaheim, Calif. Conference dates 26-28 Apr. 2012 Title of proceedings SDM 2012 : Proceedings of the 12th SIAM International Conference on Data Mining Editor(s) [Unknown] Publication date 2012 Conference series International Conference on Data Mining Start page 200 End page 212 Total pages 12 Publisher Society for Industrial and Applied Mathemations (SIAM) Place of publication Anaheim, Calif. Summary Joint analysis of multiple data sources is becoming increasingly popular in transfer learning, multi-task learning and cross-domain data mining. One promising approach to model the data jointly is through learning the shared and individual factor subspaces. However, performance of this approach depends on the subspace dimensionalities and the level of sharing needs to be specified a priori. To this end, we propose a nonparametric joint factor analysis framework for modeling multiple related data sources. Our model utilizes the hierarchical beta process as a nonparametric prior to automatically infer the number of shared and individual factors. For posterior inference, we provide a Gibbs sampling scheme using auxiliary variables. The effectiveness of the proposed framework is validated through its application on two real world problems - transfer learning in text and image retrieval. ISBN 9781611972320 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, by the Society for Industrial and Applied Mathematics Persistent URL http://hdl.handle.net/10536/DRO/DU:30049274 Document type: Conference Paper Collections: Centre for Pattern Recognition and Data Analytics 2018 ERA Submission 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 Thu, 01 Nov 2012, 13:16:09 EST Tue, 13 Nov 2012, 10:29:36 EST Tue, 13 Nov 2012, 10:33:08 EST Wed, 08 May 2013, 16:28:06 EST Thu, 09 May 2013, 08:31:28 EST Mon, 27 May 2013, 13:54:52 EST Fri, 10 Apr 2015, 08:57:42 EST Fri, 09 Oct 2015, 17:07:08 EST Mon, 12 Oct 2015, 15:13:12 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 13 times in Scopus Search Google Scholar Access Statistics: 413 Abstract Views, 7 File Downloads  -  Detailed Statistics Created: Thu, 01 Nov 2012, 13:16:09 EST

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