Tensor-variate restricted Boltzmann machines Nguyen, Tu Dinh, Tran, Truyen, Phung, Dinh and Venkatesh, Svetha 2015, Tensor-variate restricted Boltzmann machines, in AAAI 2015: The Proceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI Press, Palo Alto, Calif., pp. 2887-2893. Attached Files Name Description MIMEType Size Downloads nguyen-td-tensorvariate-2015.pdf Published version application/pdf 2.56MB 10 Title Tensor-variate restricted Boltzmann machines Author(s) Nguyen, Tu Dinh 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 AAAI Conference on Artificial Intelligence (29th : 2015 : Austin Texas) Conference location Austin, Tex. Conference dates 25-30 Jan. 2015 Title of proceedings AAAI 2015: The Proceedings of the 29th AAAI Conference on Artificial Intelligence Publication date 2015 Start page 2887 End page 2893 Total pages 7 Publisher AAAI Press Place of publication Palo Alto, Calif. Keyword(s) tensor rbm restricted boltzmann machine tvrbm multiplicative interaction eeg Summary Restricted Boltzmann Machines (RBMs) are an important class of latent variable models for representing vector data. An under-explored area is multimode data, where each data point is a matrix or a tensor. Standard RBMs applying to such data would require vectorizing matrices and tensors, thus resulting in unnecessarily high dimensionality and at the same time, destroying the inherent higher-order interaction structures. This paper introduces Tensor-variate Restricted Boltzmann Machines (TvRBMs) which generalize RBMs to capture the multiplicative interaction between data modes and the latent variables. TvRBMs are highly compact in that the number of free parameters grows only linear with the number of modes. We demonstrate the capacity of TvRBMs on three real-world applications: handwritten digit classification, face recognition and EEG-based alcoholic diagnosis. The learnt features of the model are more discriminative than the rivals, resulting in better classification performance. Notes paper No: 2887 ISBN 9781577357018 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 Full written paper - refereed ERA Research output type E Conference publication Copyright notice ©2015, The Authors Persistent URL http://hdl.handle.net/10536/DRO/DU:30076888 Document type: Conference Paper Collections: Centre for Pattern Recognition and Data Analytics Open Access Collection Related Links Link Description Publisher URL Go to link with your DU access privileges   Connect to Elements publication management system 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. 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. Versions Version Filter Type Wed, 26 Aug 2015, 14:50:13 EST Thu, 27 Aug 2015, 05:08:36 EST Thu, 27 Aug 2015, 08:51:52 EST Thu, 27 Aug 2015, 08:58:06 EST Fri, 26 Feb 2016, 17:06:51 EST Fri, 26 Feb 2016, 18:11:12 EST Tue, 01 Mar 2016, 14:28:39 EST Wed, 02 Mar 2016, 05:05:55 EST Fri, 04 Mar 2016, 08:02:20 EST Fri, 04 Mar 2016, 08:05:49 EST Wed, 09 Nov 2016, 11:24:09 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 2 times in Scopus Search Google Scholar Access Statistics: 148 Abstract Views, 12 File Downloads  -  Detailed Statistics Created: Tue, 01 Mar 2016, 14:28:39 EST

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