Learning gradients with gaussian processes

Authors:
Xinwei Jiang;Junbin Gao;Tianjiang Wang;Paul W. Kwan
Affiliations:
Intelligent and Distributed Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China;School of Computing and Mathematics, Charles Sturt University, Bathurst, NSW, Australia;Intelligent and Distributed Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China;School of Science and Technology, University of New England, Armidale, NSW, Australia
Venue:
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Year:
2010

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Abstract

The problems of variable selection and inference of statistical dependence have been addressed by modeling in the gradients learning framework based on the representer theorem In this paper, we propose a new gradients learning algorithm in the Bayesian framework, called Gaussian Processes Gradient Learning (GPGL) model, which can achieve higher accuracy while returning the credible intervals of the estimated gradients that existing methods cannot provide The simulation examples are used to verify the proposed algorithm, and its advantages can be seen from the experimental results.