A matrix factorization framework for jointly analyzing multiple nonnegative data
Nonnegative matrix factorization based methods provide one of the simplest and most effective approaches to text mining. However, their applicability is mainly limited to analyzing a single data source. In this paper, we propose a novel joint matrix factorization framework which can jointly analyze...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
Omnipress
2011
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| Online Access: | http://hdl.handle.net/20.500.11937/16617 |
| _version_ | 1848749227965939712 |
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| author | Gupta, Sunil Phung, Dinh Adams, Brett Venkatesh, Svetha |
| author2 | Michael W. Berry |
| author_facet | Michael W. Berry Gupta, Sunil Phung, Dinh Adams, Brett Venkatesh, Svetha |
| author_sort | Gupta, Sunil |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Nonnegative matrix factorization based methods provide one of the simplest and most effective approaches to text mining. However, their applicability is mainly limited to analyzing a single data source. In this paper, we propose a novel joint matrix factorization framework which can jointly analyze multiple data sources by exploiting their shared and individual structures. The proposed framework is flexible to handle any arbitrary sharing configurations encountered in real world data. We derive an efficient algorithm for learning the factorization and show that its convergence is theoretically guaranteed. We demonstrate the utility and effectiveness of the proposed framework in two real-world applications–improving social media retrieval using auxiliary sources and cross-social media retrieval. Representing each social media source using their textual tags, for both applications, we show that retrieval performance exceeds the existing state-of-the-art techniques. The proposed solution provides a generic framework and can be applicable to a wider context in data mining wherever one needs to exploit mutual and individual knowledge present across multiple data sources. |
| first_indexed | 2025-11-14T07:17:36Z |
| format | Conference Paper |
| id | curtin-20.500.11937-16617 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:17:36Z |
| publishDate | 2011 |
| publisher | Omnipress |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-166172023-01-27T05:26:31Z A matrix factorization framework for jointly analyzing multiple nonnegative data Gupta, Sunil Phung, Dinh Adams, Brett Venkatesh, Svetha Michael W. Berry Jacob Kogan Nonnegative matrix factorization based methods provide one of the simplest and most effective approaches to text mining. However, their applicability is mainly limited to analyzing a single data source. In this paper, we propose a novel joint matrix factorization framework which can jointly analyze multiple data sources by exploiting their shared and individual structures. The proposed framework is flexible to handle any arbitrary sharing configurations encountered in real world data. We derive an efficient algorithm for learning the factorization and show that its convergence is theoretically guaranteed. We demonstrate the utility and effectiveness of the proposed framework in two real-world applications–improving social media retrieval using auxiliary sources and cross-social media retrieval. Representing each social media source using their textual tags, for both applications, we show that retrieval performance exceeds the existing state-of-the-art techniques. The proposed solution provides a generic framework and can be applicable to a wider context in data mining wherever one needs to exploit mutual and individual knowledge present across multiple data sources. 2011 Conference Paper http://hdl.handle.net/20.500.11937/16617 Omnipress fulltext |
| spellingShingle | Gupta, Sunil Phung, Dinh Adams, Brett Venkatesh, Svetha A matrix factorization framework for jointly analyzing multiple nonnegative data |
| title | A matrix factorization framework for jointly analyzing multiple nonnegative data |
| title_full | A matrix factorization framework for jointly analyzing multiple nonnegative data |
| title_fullStr | A matrix factorization framework for jointly analyzing multiple nonnegative data |
| title_full_unstemmed | A matrix factorization framework for jointly analyzing multiple nonnegative data |
| title_short | A matrix factorization framework for jointly analyzing multiple nonnegative data |
| title_sort | matrix factorization framework for jointly analyzing multiple nonnegative data |
| url | http://hdl.handle.net/20.500.11937/16617 |