Hierarchical Mixture-of-Experts Model for Large-Scale Gaussian Process Regression
File(s)
Published version
Author(s)
Ng, JW
Deisenroth, MP
Type
Report
Abstract
We propose a practical and scalable Gaussian process model for large-scale nonlinear probabilistic regression. Our mixture-of-experts model is conceptually simple and hierarchically recombines computations for an overall approximation of a full Gaussian process. Closed-form and distributed computations allow for efficient and massive parallelisation while keeping the memory consumption small. Given sufficient computing resources, our model can handle arbitrarily large data sets, without explicit sparse approximations. We provide strong experimental evidence that our model can be applied to large data sets of sizes far beyond millions. Hence, our model has the potential to lay the foundation for general large-scale Gaussian process research.
Date Issued
2014-12-31
Copyright Statement
© 2014 The Authors
Description
01.04.15 KB Ok to add report to spiral, author retains copyright
Identifier
http://arxiv.org/abs/1412.3078v1