A bayesian framework for learning shared and individual subspaces from multiple data sources
This paper presents a novel Bayesian formulation to exploit shared structures across multiple data sources, constructing foundations for effective mining and retrieval across disparate domains. We jointly analyze diverse data sources using a unifying piece of metadata (textual tags). We propose a me...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
Springer
2011
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| Online Access: | http://hdl.handle.net/20.500.11937/32333 |
| _version_ | 1848753633334657024 |
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| author | Gupta, Sunil Phung, Dinh Adams, Brett Venkatesh, Svetha |
| author2 | J Z Huang |
| author_facet | J Z Huang Gupta, Sunil Phung, Dinh Adams, Brett Venkatesh, Svetha |
| author_sort | Gupta, Sunil |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper presents a novel Bayesian formulation to exploit shared structures across multiple data sources, constructing foundations for effective mining and retrieval across disparate domains. We jointly analyze diverse data sources using a unifying piece of metadata (textual tags). We propose a method based on Bayesian Probabilistic Matrix Factorization (BPMF) which is able to explicitly model the partial knowledge common to the datasets using shared subspaces and the knowledge specific to each dataset using individual subspaces. For the proposed model, we derive an efficient algorithm for learning the joint factorization based on Gibbs sampling. The effectiveness of the model is demonstrated by social media retrieval tasks across single and multiple media. The proposed solution is applicable to a wider context, providing a formal framework suitable for exploiting individual as well as mutual knowledge present across heterogeneous data sources of many kinds. |
| first_indexed | 2025-11-14T08:27:37Z |
| format | Conference Paper |
| id | curtin-20.500.11937-32333 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:27:37Z |
| publishDate | 2011 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-323332023-01-27T05:52:09Z A bayesian framework for learning shared and individual subspaces from multiple data sources Gupta, Sunil Phung, Dinh Adams, Brett Venkatesh, Svetha J Z Huang L Cao J Srivastava This paper presents a novel Bayesian formulation to exploit shared structures across multiple data sources, constructing foundations for effective mining and retrieval across disparate domains. We jointly analyze diverse data sources using a unifying piece of metadata (textual tags). We propose a method based on Bayesian Probabilistic Matrix Factorization (BPMF) which is able to explicitly model the partial knowledge common to the datasets using shared subspaces and the knowledge specific to each dataset using individual subspaces. For the proposed model, we derive an efficient algorithm for learning the joint factorization based on Gibbs sampling. The effectiveness of the model is demonstrated by social media retrieval tasks across single and multiple media. The proposed solution is applicable to a wider context, providing a formal framework suitable for exploiting individual as well as mutual knowledge present across heterogeneous data sources of many kinds. 2011 Conference Paper http://hdl.handle.net/20.500.11937/32333 10.1007/978-3-642-20841-6_12 Springer restricted |
| spellingShingle | Gupta, Sunil Phung, Dinh Adams, Brett Venkatesh, Svetha A bayesian framework for learning shared and individual subspaces from multiple data sources |
| title | A bayesian framework for learning shared and individual subspaces from multiple data sources |
| title_full | A bayesian framework for learning shared and individual subspaces from multiple data sources |
| title_fullStr | A bayesian framework for learning shared and individual subspaces from multiple data sources |
| title_full_unstemmed | A bayesian framework for learning shared and individual subspaces from multiple data sources |
| title_short | A bayesian framework for learning shared and individual subspaces from multiple data sources |
| title_sort | bayesian framework for learning shared and individual subspaces from multiple data sources |
| url | http://hdl.handle.net/20.500.11937/32333 |