Regularised nonnegative shared subspace learning
Joint modeling of related data sources has the potential to improve various data mining tasks such as transfer learning, multitask clustering, information retrieval etc. However, diversity among various data sources might outweigh the advantages of the joint modeling, and thus may result in performa...
| Main Authors: | , , , |
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| Format: | Journal Article |
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Springer
2011
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| Online Access: | http://hdl.handle.net/20.500.11937/6219 |
| _version_ | 1848745013414985728 |
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| author | Gupta, Sunil Phung, Dinh Adams, Brett Venkatesh, Svetha |
| author_facet | Gupta, Sunil Phung, Dinh Adams, Brett Venkatesh, Svetha |
| author_sort | Gupta, Sunil |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Joint modeling of related data sources has the potential to improve various data mining tasks such as transfer learning, multitask clustering, information retrieval etc. However, diversity among various data sources might outweigh the advantages of the joint modeling, and thus may result in performance degradations. To this end, we propose a regularized shared subspace learning framework, which can exploit the mutual strengths of related data sources while being immune to the effects of the variabilities of each source. This is achieved by further imposing a mutual orthogonality constraint on the constituent subspaces which segregates the common patterns from the source specific patterns, and thus, avoids performance degradations. Our approach is rooted in nonnegative matrix factorization and extends it further to enable joint analysis of related data sources. Experiments performed using three real world data sets for both retrieval and clustering applications demonstrate the benefits of regularization and validate the effectiveness of the model. Our proposed solution provides a formal framework appropriate for jointly analyzing related data sources and therefore, it is applicable to a wider context in data mining. |
| first_indexed | 2025-11-14T06:10:36Z |
| format | Journal Article |
| id | curtin-20.500.11937-6219 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:10:36Z |
| publishDate | 2011 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-62192017-09-13T16:08:46Z Regularised nonnegative shared subspace learning Gupta, Sunil Phung, Dinh Adams, Brett Venkatesh, Svetha Nonnegative shared subspace learning Auxiliary sources Transfer learning Multi-task clustering Joint modeling of related data sources has the potential to improve various data mining tasks such as transfer learning, multitask clustering, information retrieval etc. However, diversity among various data sources might outweigh the advantages of the joint modeling, and thus may result in performance degradations. To this end, we propose a regularized shared subspace learning framework, which can exploit the mutual strengths of related data sources while being immune to the effects of the variabilities of each source. This is achieved by further imposing a mutual orthogonality constraint on the constituent subspaces which segregates the common patterns from the source specific patterns, and thus, avoids performance degradations. Our approach is rooted in nonnegative matrix factorization and extends it further to enable joint analysis of related data sources. Experiments performed using three real world data sets for both retrieval and clustering applications demonstrate the benefits of regularization and validate the effectiveness of the model. Our proposed solution provides a formal framework appropriate for jointly analyzing related data sources and therefore, it is applicable to a wider context in data mining. 2011 Journal Article http://hdl.handle.net/20.500.11937/6219 10.1007/s10618-011-0244-8 Springer restricted |
| spellingShingle | Nonnegative shared subspace learning Auxiliary sources Transfer learning Multi-task clustering Gupta, Sunil Phung, Dinh Adams, Brett Venkatesh, Svetha Regularised nonnegative shared subspace learning |
| title | Regularised nonnegative shared subspace learning |
| title_full | Regularised nonnegative shared subspace learning |
| title_fullStr | Regularised nonnegative shared subspace learning |
| title_full_unstemmed | Regularised nonnegative shared subspace learning |
| title_short | Regularised nonnegative shared subspace learning |
| title_sort | regularised nonnegative shared subspace learning |
| topic | Nonnegative shared subspace learning Auxiliary sources Transfer learning Multi-task clustering |
| url | http://hdl.handle.net/20.500.11937/6219 |