Latent Wishart Processes for Relational Kernel Learning
; Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, PMLR 5:336-343, 2009.
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 diverse real-world applications, we demonstrate that our LWP model can give very promising performance in practice.