Mixed-Variate Restricted Boltzmann Machines
Modern datasets are becoming heterogeneous. To this end, we present in this paper Mixed- Variate Restricted Boltzmann Machines for simultaneously modelling variables of multiple types and modalities, including binary and continuous responses, categorical options, multicategorical choices, ordinal as...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
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JMLR
2011
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| Online Access: | http://jmlr.csail.mit.edu/proceedings/papers/v20/tran11/tran11.pdf http://hdl.handle.net/20.500.11937/34741 |
| _version_ | 1848754306043346944 |
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| author | Tran, Truyen Phung, Dinh Venkatesh, Svetha |
| author2 | Chun-Nan Hsu |
| author_facet | Chun-Nan Hsu Tran, Truyen Phung, Dinh Venkatesh, Svetha |
| author_sort | Tran, Truyen |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Modern datasets are becoming heterogeneous. To this end, we present in this paper Mixed- Variate Restricted Boltzmann Machines for simultaneously modelling variables of multiple types and modalities, including binary and continuous responses, categorical options, multicategorical choices, ordinal assessment and category-ranked preferences. Dependency among variables is modeled using latent binary variables, each of which can be interpreted as a particular hidden aspect of the data. The proposed model, similar to the standard RBMs, allows fast evaluation of the posterior for the latent variables. Hence, it is naturally suitable for many common tasks including, but not limited to, (a) as a pre-processing step to convert complex input data into a more convenient vectorial representation through the latent posteriors, thereby offering a dimensionality reduction capacity, (b) as a classier supporting binary, multiclass, multilabel, and label-ranking outputs, or a regression tool for continuous outputs and (c) as a data completion tool for multimodal and heterogeneous data. We evaluate the proposed model on a large-scale dataset using the world opinion survey results on three tasks: feature extraction and visualization, data completion and prediction. |
| first_indexed | 2025-11-14T08:38:18Z |
| format | Conference Paper |
| id | curtin-20.500.11937-34741 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:38:18Z |
| publishDate | 2011 |
| publisher | JMLR |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-347412023-01-27T05:52:10Z Mixed-Variate Restricted Boltzmann Machines Tran, Truyen Phung, Dinh Venkatesh, Svetha Chun-Nan Hsu Wee Sun Lee Modern datasets are becoming heterogeneous. To this end, we present in this paper Mixed- Variate Restricted Boltzmann Machines for simultaneously modelling variables of multiple types and modalities, including binary and continuous responses, categorical options, multicategorical choices, ordinal assessment and category-ranked preferences. Dependency among variables is modeled using latent binary variables, each of which can be interpreted as a particular hidden aspect of the data. The proposed model, similar to the standard RBMs, allows fast evaluation of the posterior for the latent variables. Hence, it is naturally suitable for many common tasks including, but not limited to, (a) as a pre-processing step to convert complex input data into a more convenient vectorial representation through the latent posteriors, thereby offering a dimensionality reduction capacity, (b) as a classier supporting binary, multiclass, multilabel, and label-ranking outputs, or a regression tool for continuous outputs and (c) as a data completion tool for multimodal and heterogeneous data. We evaluate the proposed model on a large-scale dataset using the world opinion survey results on three tasks: feature extraction and visualization, data completion and prediction. 2011 Conference Paper http://hdl.handle.net/20.500.11937/34741 http://jmlr.csail.mit.edu/proceedings/papers/v20/tran11/tran11.pdf JMLR restricted |
| spellingShingle | Tran, Truyen Phung, Dinh Venkatesh, Svetha Mixed-Variate Restricted Boltzmann Machines |
| title | Mixed-Variate Restricted Boltzmann Machines |
| title_full | Mixed-Variate Restricted Boltzmann Machines |
| title_fullStr | Mixed-Variate Restricted Boltzmann Machines |
| title_full_unstemmed | Mixed-Variate Restricted Boltzmann Machines |
| title_short | Mixed-Variate Restricted Boltzmann Machines |
| title_sort | mixed-variate restricted boltzmann machines |
| url | http://jmlr.csail.mit.edu/proceedings/papers/v20/tran11/tran11.pdf http://hdl.handle.net/20.500.11937/34741 |