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Latent Wishart processes for relational kernel learning

Authors Li, Wu-Jun HKUST affiliated (currently or previously)
Zhang, Zhihua HKUST affiliated (currently or previously)
Yeung, Dit Yan View this author's profile
Issue Date 2009
Source Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS), Clearwater Beach, Florida, USA , 16-18 April 2009, p. 336-343
Summary One main concern towards kernel classifiers is on their sensitivity to the choice of kernel function or kernel matrix which characterizes the similarity between instances. Many real-world data, such as web pages and protein-protein interaction data, are relational in nature in the sense that different instances are correlated (linked) with each other. The relational information available in such data often provides strong hints on the correlation (or similarity) between instances. In this paper, we propose a novel relational kernel learning model based on latent Wishart processes (LWP) to learn the kernel function for relational data. This is done by seamlessly integrating the relational information and the input attributes into the kernel learning process. Through extensive experiments on real-world applications, we demonstrate that our LWP model can give very promising performance in practice.
Rights Reprinted with permission from MIT Press
Language English
Format Conference paper
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