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http://hdl.handle.net/1783.1/2483
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| Title: | Bayesian inference for transductive learning of kernel matrix using the Tanner-Wong data augmentation algorithm |
| Authors: | Zhang, Zhihua
Yeung, Dit-Yan
Kwok, James Tin-Yau |
| Keywords: | Kernel methods
Tanner-Wong data augmentation algorithm
Bayesian hierarchical model |
| Issue Date: | 2004 |
| Citation: | Proceedings of the 21st International Conference on Machine Learning, Banff, Alberta, Canada, 4-8 July 2004, p.935-942 |
| Abstract: | In kernel methods, an interesting recent development seeks to learn a good kernel from empirical data automatically. In this paper, by regarding the transductive learning of the kernel matrix as a missing data problem, we propose a Bayesian hierarchical model for the problem and devise the Tanner-Wong data augmentation algorithm for making inference on the model. The Tanner-Wong algorithm is closely related to Gibbs sampling, and it also bears a strong resemblance to the expectation-maximization (EM) algorithm. For an efficient implementation, we propose a simplified Bayesian hierarchical model and the corresponding Tanner-Wong algorithm. We express the relationship between the kernel on the input space and the kernel on the output space as a symmetric-definite generalized eigenproblem. Based on this eigenproblem, an efficient approach to choosing the base kernel matrices is presented. The effctiveness of our Bayesian model with the Tanner-Wong algorithm is demonstrated through some classification experiments showing promising results. |
| Rights: | © ACM, 2004. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. |
| URI: | http://hdl.handle.net/1783.1/2483 |
| Appears in Collections: | CSE Conference Papers
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| yeung.icml2004b.pdf | pre-published version | 163Kb | Adobe PDF | View/Open |
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