Choudhury, A., Nair, P.B. and Keane, A.J.
A data parallel approach for large-scale Gaussian process modelling
In Proceedings of the Second SIAM International Conference on Data Mining.
This paper proposes an enabling data parallel local learning methodology for handling
large data regression through the Gaussian Process (GP) modeling paradigm.
The proposed model achieves parallelism by employing a specialized compactly
supported covariance function defined over spatially localized clusters. The associated
load balancing constraints arising from data parallelism are satisfied using a
novel greedy clustering algorithm, GeoClust producing balanced clusters localized
in space. Further, the use of the proposed covariance function as a building block
for GP models is shown to decompose the maximum likelihood estimation problem
into smaller decoupled subproblems. The attendant benefits which include a significant
reduction in training complexity, as well as sparse predictive models for the
posterior mean and variance make the present scheme extremely attractive. Experimental
investigations on real and synthetic data demonstrate that the current
approach can consistently outperform the state-of-the-art Bayesian Committee Machine
(BCM) which employs a random data partitioning strategy. Finally, extensive
evaluations over a grid-based computational infrastructure using the NetSolve distributed
computing system show that the present approach scales well with data
and could potentially be used in large-scale data mining applications.
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