Thurstonian Boltzmann machines: Learning from multiple inequalities
dc.contributor.author | Tran, The Truyen | |
dc.contributor.author | Phung, D. | |
dc.contributor.author | Venkatesh, S. | |
dc.date.accessioned | 2017-01-30T11:03:39Z | |
dc.date.available | 2017-01-30T11:03:39Z | |
dc.date.created | 2015-10-29T04:09:59Z | |
dc.date.issued | 2013 | |
dc.identifier.citation | Tran, T.T. and Phung, D. and Venkatesh, S. 2013. Thurstonian Boltzmann machines: Learning from multiple inequalities, pp. 705-713: International Machine Learning Society (IMLS). | |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/7956 | |
dc.description.abstract |
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). | |
dc.publisher | International Machine Learning Society (IMLS) | |
dc.title | Thurstonian Boltzmann machines: Learning from multiple inequalities | |
dc.type | Conference Paper | |
dcterms.source.number | PART 1 | |
dcterms.source.startPage | 705 | |
dcterms.source.endPage | 713 | |
dcterms.source.title | 30th International Conference on Machine Learning, ICML 2013 | |
dcterms.source.series | 30th International Conference on Machine Learning, ICML 2013 | |
curtin.department | Multi-Sensor Proc & Content Analysis Institute | |
curtin.accessStatus | Fulltext not available |
Files in this item
Files | Size | Format | View |
---|---|---|---|
There are no files associated with this item. |