Thurstonian Boltzmann machines: learning from multiple inequalities Tran, Truyen, Phung, Dinh and Venkatesh, Svetha 2013, Thurstonian Boltzmann machines: learning from multiple inequalities, in ICML 2013 : Proceedings of the Machine Learning 2013 International Conference, International Machine Learning Society (IMLS), [Atlanta, Ga.], pp. 46-54. Attached Files Name Description MIMEType Size Downloads Title Thurstonian Boltzmann machines: learning from multiple inequalities Author(s) Tran, Truyen orcid.org/0000-0001-6531-8907 Phung, Dinh orcid.org/0000-0002-9977-8247 Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Conference name Machine Learning. International Conference (30th : 2013 : Atlanta, Ga.) Conference location Atlanta, Ga. Conference dates 16-21 Jun. 2013 Title of proceedings ICML 2013 : Proceedings of the Machine Learning 2013 International Conference Editor(s) Dasgupta, S. McAllester,D Publication date 2013 Series JMLR workshop and conference proceeding v.28 Start page 46 End page 54 Total pages 9 Publisher International Machine Learning Society (IMLS) Place of publication [Atlanta, Ga.] Summary We introduce Thurstonian Boltzmann Machines (TBM), a unified architecture that can naturally incorporate a wide range of data inputs at the same time. Our motivation rests in the Thurstonian view that many discrete data types can be considered as being generated from a subset of underlying latent continuous variables, and in the observation that each realisation of a discrete type imposes certain inequalities on those variables. Thus learning and inference in TBM reduce to making sense of a set of inequalities. Our proposed TBM naturally supports the following types: Gaussian, intervals, censored, binary, categorical, muticategorical, ordinal, (in)-complete rank with and without ties. We demonstrate the versatility and capacity of the proposed model on three applications of very different natures; namely handwritten digit recognition, collaborative filtering and complex social survey analysis. Copyright 2013 by the author(s). Language eng Field of Research 080109 Pattern Recognition and Data Mining Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category E1.1 Full written paper - refereed ERA Research output type E Conference publication Copyright notice ©2013, IMLS Persistent URL http://hdl.handle.net/10536/DRO/DU:30074722 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment Centre for Pattern Recognition and Data Analytics (PRADA) 2018 ERA Submission Related Links Link Description Publisher URL 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 Tue, 28 Jul 2015, 09:30:14 EST Wed, 29 Jul 2015, 05:00:07 EST Wed, 29 Jul 2015, 08:28:12 EST Tue, 17 May 2016, 15:40:34 EST Wed, 18 May 2016, 06:21:10 EST Fri, 03 Jun 2016, 09:43:24 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 8 times in Scopus Search Google Scholar Access Statistics: 553 Abstract Views, 6 File Downloads  -  Detailed Statistics Created: Tue, 17 May 2016, 15:40:34 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.