Online Coregularization for Multiview Semisupervised Learning
Joint Authors
Sun, Boliang
Li, Guohui
Jia, Li
Huang, Kuihua
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-09-08
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Sun, Boliang& Li, Guohui& Jia, Li& Huang, Kuihua. 2013. Online Coregularization for Multiview Semisupervised Learning. The Scientific World Journal،Vol. 2013, no. 2013, pp.1-15.
https://search.emarefa.net/detail/BIM-1032873
Modern Language Association (MLA)
Sun, Boliang…[et al.]. Online Coregularization for Multiview Semisupervised Learning. The Scientific World Journal No. 2013 (2013), pp.1-15.
https://search.emarefa.net/detail/BIM-1032873
American Medical Association (AMA)
Sun, Boliang& Li, Guohui& Jia, Li& Huang, Kuihua. Online Coregularization for Multiview Semisupervised Learning. The Scientific World Journal. 2013. Vol. 2013, no. 2013, pp.1-15.
https://search.emarefa.net/detail/BIM-1032873
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1032873