Latent Wishart Processes for Relational Kernel Learning

Wu-Jun Li, Zhihua Zhang, Dit-Yan Yeung
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, PMLR 5:336-343, 2009.

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 this Paper

BibTeX
@InProceedings{pmlr-v5-li09b, 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}, pages = {336--343}, year = {2009}, editor = {van Dyk, David and Welling, Max}, volume = {5}, series = {Proceedings of Machine Learning Research}, address = {Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v5/li09b/li09b.pdf}, url = {https://proceedings.mlr.press/v5/li09b.html}, 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.} }
Endnote
%0 Conference Paper %T Latent Wishart Processes for Relational Kernel Learning %A Wu-Jun Li %A Zhihua Zhang %A Dit-Yan Yeung %B Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2009 %E David van Dyk %E Max Welling %F pmlr-v5-li09b %I PMLR %P 336--343 %U https://proceedings.mlr.press/v5/li09b.html %V 5 %X 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.
RIS
TY - CPAPER TI - Latent Wishart Processes for Relational Kernel Learning AU - Wu-Jun Li AU - Zhihua Zhang AU - Dit-Yan Yeung BT - Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics DA - 2009/04/15 ED - David van Dyk ED - Max Welling ID - pmlr-v5-li09b PB - PMLR DP - Proceedings of Machine Learning Research VL - 5 SP - 336 EP - 343 L1 - http://proceedings.mlr.press/v5/li09b/li09b.pdf UR - https://proceedings.mlr.press/v5/li09b.html AB - 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. ER -
APA
Li, W., Zhang, Z. & Yeung, D.. (2009). Latent Wishart Processes for Relational Kernel Learning. Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 5:336-343 Available from https://proceedings.mlr.press/v5/li09b.html.

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