ML Anthology

Generalized Online Sparse Gaussian Processes with Application to Persistent Mobile Robot Localization

Kian Hsiang Low, Nuo Xu, Jie Chen, Keng Kiat Lim, Etkin Baris Özgül
ECML-PKDD 2014 pp. 499-503
doi:10.1007/978-3-662-44845-8_44 /ecmlpkdd/2014/low2014ecmlpkdd-generalized/

Abstract

This paper presents a novel online sparse Gaussian process (GP) approximation method [3] that is capable of achieving constant time and memory (i.e., independent of the size of the data) per time step. We theoretically guarantee its predictive performance to be equivalent to that of a sophisticated offline sparse GP approximation method. We empirically demonstrate the practical feasibility of using our online sparse GP approximation method through a real-world persistent mobile robot localization experiment.

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Cite

Text

Low et al. "Generalized Online Sparse Gaussian Processes with Application to Persistent Mobile Robot Localization." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2014. doi:10.1007/978-3-662-44845-8_44

Markdown

[Low et al. "Generalized Online Sparse Gaussian Processes with Application to Persistent Mobile Robot Localization." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2014.](https://mlanthology.org/ecmlpkdd/2014/low2014ecmlpkdd-generalized/) doi:10.1007/978-3-662-44845-8_44

BibTeX

@inproceedings{low2014ecmlpkdd-generalized,
  title     = {{Generalized Online Sparse Gaussian Processes with Application to Persistent Mobile Robot Localization}},
  author    = {Low, Kian Hsiang and Xu, Nuo and Chen, Jie and Lim, Keng Kiat and Özgül, Etkin Baris},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2014},
  pages     = {499-503},
  doi       = {10.1007/978-3-662-44845-8_44},
  url       = {https://mlanthology.org/ecmlpkdd/2014/low2014ecmlpkdd-generalized/}
}