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Cumulative restricted Boltzmann machines for ordinal matrix data analysis

Tran, Truyen, Phung, Dinh and Venkatesh, Svetha 2012, Cumulative restricted Boltzmann machines for ordinal matrix data analysis, in ACML 2012 : Proceedings of the 4th Asian Conference on Machine Learning, JMLR : workshop and conference proceedings, [Singapore], pp. 411-426.

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Title Cumulative restricted Boltzmann machines for ordinal matrix data analysis
Author(s) Tran, TruyenORCID iD for Tran, Truyen orcid.org/0000-0001-6531-8907
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name Asian Conference on Machine Learning (4th : 2012 : Singapore)
Conference location Singapore
Conference dates 4-6 Nov. 2012
Title of proceedings ACML 2012 : Proceedings of the 4th Asian Conference on Machine Learning
Editor(s) Hoi, Steven C.H.
Buntine, Wray
Publication date 2012
Conference series Asian Conference on Machine Learning
Start page 411
End page 426
Total pages 16
Publisher JMLR : workshop and conference proceedings
Place of publication [Singapore]
Keyword(s) cumulative restricted Boltzmann machine
ordinal analysis
matrix data
Summary Ordinal data is omnipresent in almost all multiuser-generated feedback - questionnaires, preferences etc. This paper investigates modelling of ordinal data with Gaussian restricted Boltzmann machines (RBMs). In particular, we present the model architecture, learning and inference procedures for both vector-variate and matrix-variate ordinal data. We show that our model is able to capture latent opinion profile of citizens around the world, and is competitive against state-of-art collaborative filtering techniques on large-scale public datasets. The model thus has the potential to extend application of RBMs to diverse domains such as recommendation systems, product reviews and expert assessments.
Language eng
Field of Research 120403 Engineering Design Methods
Socio Economic Objective 899999 Information and Communication Services not elsewhere classified
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2012, The Authors
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30052641

Document type: Conference Paper
Collections: Centre for Pattern Recognition and Data Analytics
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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.