Lazy Gaussian Process Committee for Real-Time Online Regression
Abstract
A significant problem of Gaussian process (GP) is its unfavorable scaling with a large amount of data. To overcome this issue, we present a novel GP approximation scheme for online regression. Our model is based on a combination of multiple GPs with random hyperparameters. The model is trained by incrementally allocating new examples to a selected subset of GPs. The selection is carried out efficiently by optimizing a submodular function. Experiments on real-world data sets showed that our method outperforms existing online GP regression methods in both accuracy and efficiency. The applicability of the proposed method is demonstrated by the mouse-trajectory prediction in an Internet banking scenario.
Cite
Text
Xiao and Eckert. "Lazy Gaussian Process Committee for Real-Time Online Regression." AAAI Conference on Artificial Intelligence, 2013. doi:10.1609/AAAI.V27I1.8572Markdown
[Xiao and Eckert. "Lazy Gaussian Process Committee for Real-Time Online Regression." AAAI Conference on Artificial Intelligence, 2013.](https://mlanthology.org/aaai/2013/xiao2013aaai-lazy/) doi:10.1609/AAAI.V27I1.8572BibTeX
@inproceedings{xiao2013aaai-lazy,
title = {{Lazy Gaussian Process Committee for Real-Time Online Regression}},
author = {Xiao, Han and Eckert, Claudia},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2013},
pages = {969-976},
doi = {10.1609/AAAI.V27I1.8572},
url = {https://mlanthology.org/aaai/2013/xiao2013aaai-lazy/}
}