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Bayesian inference for transductive learning of kernel matrix using the tanner-wong data augmentation algorithm

Authors Zhang, Zhihua HKUST affiliated (currently or previously)
Yeung, Dityan View this author's profile
Kwok, James Tin-Yau View this author's profile
Issue Date 2004
Source Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004, Banff, Alta, Canada, 4 - 8 July 2004, Code 64339 , 2004, p. 935-942
Summary 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 effectiveness of our Bayesian model with the Tanner-Wong algorithm is demonstrated through some classification experiments showing promising results.
ISBN 1581138385
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.
Language English
Format Conference paper
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