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.

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Title Thurstonian Boltzmann machines: learning from multiple inequalities
Author(s) Tran, TruyenORCID iD for Tran, Truyen
Phung, DinhORCID iD for Phung, Dinh
Venkatesh, SvethaORCID iD for Venkatesh, Svetha
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.
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
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