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
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).
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eng
Field of Research
080109 Pattern Recognition and Data Mining
Socio Economic Objective
970108 Expanding Knowledge in the Information and Computing Sciences
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