Identification of Outliers in Graph Signals*
Name
GopalakrishnanLiBalakrishnan_CDC2019.pdf
Description
Accepted version
Size
1.77 MB
Format
Unknown
Checksum (MD5)
9f2da28b36f7deb6c71ca134de904c3d
Author(s)
Gopalakrishnan, Karthik
Li, Max Z.
Balakrishnan, Hamsa
Date Issued
December 2019
Journal
Proceedings of the IEEE Conference on Decision and Control
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
2019. "Identification of Outliers in Graph Signals*." Proceedings of the IEEE Conference on Decision and Control, 2019-December.
Version
Author's final manuscript
Abstract
© 2019 IEEE. Outlier detection, or the identification of observations that differ significantly from the norm, is an important aspect of data mining. Conventional outlier detection tools have limited applicability to networks, in which there are interdependencies between the variables. In this paper, we consider the problem of identifying unusual spatial distributions of nodal signals on a graph. Leveraging tools from graph signal processing and statistical analysis, we propose a methodology to identify outliers in graph signals in a computationally efficient manner. Specifically, we examine a projection of the graph signal into a lower dimensional representation that enables easier outlier identification. Additionally, we derive analytical expressions for the outlier bounds. We apply our technique by identifying off-nominal days in the context of the US airport network using aviation delay data.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/CDC40024.2019.9029478
