Latent Wishart Processes for Relational Kernel Learning

Wu-Jun Li, Zhihua Zhang, Dit-Yan Yeung
; Proceedings of the Twelth 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 = {Wu-Jun Li and Zhihua Zhang and Dit-Yan Yeung}, booktitle = {Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics}, pages = {336--343}, year = {2009}, editor = {David van Dyk and Max Welling}, 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 = {http://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 Twelth 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 %J Proceedings of Machine Learning Research %P 336--343 %U http://proceedings.mlr.press %V 5 %W PMLR %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 Twelth International Conference on Artificial Intelligence and Statistics PY - 2009/04/15 DA - 2009/04/15 ED - David van Dyk ED - Max Welling ID - pmlr-v5-li09b PB - PMLR SP - 336 DP - PMLR EP - 343 L1 - http://proceedings.mlr.press/v5/li09b/li09b.pdf UR - http://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 Twelth International Conference on Artificial Intelligence and Statistics, in PMLR 5:336-343

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