K-Means in Space: A Radiation Sensitivity Evaluation
Abstract
Spacecraft are increasingly making use of onboard data analysis to inform additional data collection and prioritization decisions. However, many spacecraft operate in high-radiation environments in which the reliability of data-intensive computation is not known. This paper presents the first study of radiation sensitivity for k-means clustering. Our key findings are that 1) k-means data structures differ in sensitivity, and sensitivity is not determined by the amount of memory exposed, 2) no special radiation protection is needed below a data-set-dependent radiation threshold, enabling the use of faster, smaller, and cheaper onboard memory in some cases, and 3) subsampling improves radiation tolerance slightly, but the use of kd-trees unfortunately reduces tolerance. Our conclusions can be used to tailor k-means for future use in high-radiation environments.
Cite
Text
Wagstaff and Bornstein. "K-Means in Space: A Radiation Sensitivity Evaluation." International Conference on Machine Learning, 2009. doi:10.1145/1553374.1553514Markdown
[Wagstaff and Bornstein. "K-Means in Space: A Radiation Sensitivity Evaluation." International Conference on Machine Learning, 2009.](https://mlanthology.org/icml/2009/wagstaff2009icml-k/) doi:10.1145/1553374.1553514BibTeX
@inproceedings{wagstaff2009icml-k,
title = {{K-Means in Space: A Radiation Sensitivity Evaluation}},
author = {Wagstaff, Kiri L. and Bornstein, Benjamin J.},
booktitle = {International Conference on Machine Learning},
year = {2009},
pages = {1097-1104},
doi = {10.1145/1553374.1553514},
url = {https://mlanthology.org/icml/2009/wagstaff2009icml-k/}
}