Latent Wishart Processes for Relational Kernel Learning
Abstract
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.
Cite
Text
Li et al. "Latent Wishart Processes for Relational Kernel Learning." Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009.Markdown
[Li et al. "Latent Wishart Processes for Relational Kernel Learning." Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009.](https://mlanthology.org/aistats/2009/li2009aistats-latent/)BibTeX
@inproceedings{li2009aistats-latent,
title = {{Latent Wishart Processes for Relational Kernel Learning}},
author = {Li, Wu-Jun and Zhang, Zhihua and Yeung, Dit-Yan},
booktitle = {Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics},
year = {2009},
pages = {336-343},
volume = {5},
url = {https://mlanthology.org/aistats/2009/li2009aistats-latent/}
}