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Latent Wishart Processes for Relational Kernel Learning

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 realworld data, such as web pages and proteinprotein interaction data, are relational in nature in the sense that dierent 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 realworld applications, we demonstrate that our LWP model can give very promising performance in practice.