Online Coregularization for Multiview Semisupervised Learning
We propose a novel online coregularization framework for multiview semisupervised learning based on the notion of duality in constrained optimization. Using the weak duality theorem, we reduce the online coregularization to the task of increasing the dual function. We demonstrate that the existing o...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2013/398146 |
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| _version_ | 1832558313974267904 |
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| author | Boliang Sun Guohui Li Li Jia Kuihua Huang |
| author_facet | Boliang Sun Guohui Li Li Jia Kuihua Huang |
| author_sort | Boliang Sun |
| collection | DOAJ |
| description | We propose a novel online coregularization framework for multiview semisupervised learning based on the notion of duality in constrained optimization. Using the weak duality theorem, we reduce the online coregularization to the task of increasing the dual function. We demonstrate that the existing online coregularization algorithms in previous work can be viewed as an approximation of our dual ascending process using gradient ascent. New algorithms are derived based on the idea of ascending the dual function more aggressively. For practical purpose, we also propose two sparse approximation approaches for kernel representation to reduce the computational complexity. Experiments show that our derived online coregularization algorithms achieve risk and accuracy comparable to offline algorithms while consuming less time and memory. Specially, our online coregularization algorithms are able to deal with concept drift and maintain a much smaller error rate. This paper paves a way to the design and analysis of online coregularization algorithms. |
| format | Article |
| id | doaj-art-4c777fc817c04a13b8b43d37a9b6cdb4 |
| institution | Kabale University |
| issn | 1537-744X |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-4c777fc817c04a13b8b43d37a9b6cdb42025-02-03T01:32:46ZengWileyThe Scientific World Journal1537-744X2013-01-01201310.1155/2013/398146398146Online Coregularization for Multiview Semisupervised LearningBoliang Sun0Guohui Li1Li Jia2Kuihua Huang3College of Information System and Management, National University of Defense Technology, Changsha, Hunan 410073, ChinaCollege of Information System and Management, National University of Defense Technology, Changsha, Hunan 410073, ChinaCollege of Information System and Management, National University of Defense Technology, Changsha, Hunan 410073, ChinaCollege of Information System and Management, National University of Defense Technology, Changsha, Hunan 410073, ChinaWe propose a novel online coregularization framework for multiview semisupervised learning based on the notion of duality in constrained optimization. Using the weak duality theorem, we reduce the online coregularization to the task of increasing the dual function. We demonstrate that the existing online coregularization algorithms in previous work can be viewed as an approximation of our dual ascending process using gradient ascent. New algorithms are derived based on the idea of ascending the dual function more aggressively. For practical purpose, we also propose two sparse approximation approaches for kernel representation to reduce the computational complexity. Experiments show that our derived online coregularization algorithms achieve risk and accuracy comparable to offline algorithms while consuming less time and memory. Specially, our online coregularization algorithms are able to deal with concept drift and maintain a much smaller error rate. This paper paves a way to the design and analysis of online coregularization algorithms.http://dx.doi.org/10.1155/2013/398146 |
| spellingShingle | Boliang Sun Guohui Li Li Jia Kuihua Huang Online Coregularization for Multiview Semisupervised Learning The Scientific World Journal |
| title | Online Coregularization for Multiview Semisupervised Learning |
| title_full | Online Coregularization for Multiview Semisupervised Learning |
| title_fullStr | Online Coregularization for Multiview Semisupervised Learning |
| title_full_unstemmed | Online Coregularization for Multiview Semisupervised Learning |
| title_short | Online Coregularization for Multiview Semisupervised Learning |
| title_sort | online coregularization for multiview semisupervised learning |
| url | http://dx.doi.org/10.1155/2013/398146 |
| work_keys_str_mv | AT boliangsun onlinecoregularizationformultiviewsemisupervisedlearning AT guohuili onlinecoregularizationformultiviewsemisupervisedlearning AT lijia onlinecoregularizationformultiviewsemisupervisedlearning AT kuihuahuang onlinecoregularizationformultiviewsemisupervisedlearning |