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 co...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
International Machine Learning Society (IMLS)
2013
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| Online Access: | http://hdl.handle.net/20.500.11937/7956 |
| _version_ | 1848745518176403456 |
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| author | Tran, The Truyen Phung, D. Venkatesh, S. |
| author_facet | Tran, The Truyen Phung, D. Venkatesh, S. |
| author_sort | Tran, The Truyen |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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). |
| first_indexed | 2025-11-14T06:18:38Z |
| format | Conference Paper |
| id | curtin-20.500.11937-7956 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:18:38Z |
| publishDate | 2013 |
| publisher | International Machine Learning Society (IMLS) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-79562017-01-30T11:03:39Z Thurstonian Boltzmann machines: Learning from multiple inequalities Tran, The Truyen Phung, D. Venkatesh, S. 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). 2013 Conference Paper http://hdl.handle.net/20.500.11937/7956 International Machine Learning Society (IMLS) restricted |
| spellingShingle | Tran, The Truyen Phung, D. Venkatesh, S. Thurstonian Boltzmann machines: Learning from multiple inequalities |
| title | Thurstonian Boltzmann machines: Learning from multiple inequalities |
| title_full | Thurstonian Boltzmann machines: Learning from multiple inequalities |
| title_fullStr | Thurstonian Boltzmann machines: Learning from multiple inequalities |
| title_full_unstemmed | Thurstonian Boltzmann machines: Learning from multiple inequalities |
| title_short | Thurstonian Boltzmann machines: Learning from multiple inequalities |
| title_sort | thurstonian boltzmann machines: learning from multiple inequalities |
| url | http://hdl.handle.net/20.500.11937/7956 |