Type
Conference PaperKAUST Grant Number
OSR-2015-Sensors-2700Date
2017-03-06Online Publication Date
2017-03-06Print Publication Date
2016-11Permanent link to this record
http://hdl.handle.net/10754/623599
Metadata
Show full item recordAbstract
In this paper we develop statistical detection theory for graph signals. In particular, given two graphs, namely, a background graph that represents an usual activity and an alternative graph that represents some unusual activity, we are interested in answering the following question: To which of the two graphs does the observed graph signal fit the best? To begin with, we assume both the graphs are known, and derive an optimal Neyman-Pearson detector. Next, we derive a suboptimal detector for the case when the alternative graph is not known. The developed theory is illustrated with numerical experiments.Citation
Chepuri SP, Leus G (2016) Subgraph detection using graph signals. 2016 50th Asilomar Conference on Signals, Systems and Computers. Available: http://dx.doi.org/10.1109/acssc.2016.7869097.Sponsors
This work was supported by the KAUST-MIT-TUD consortium grant OSR-2015-Sensors-2700.Conference/Event name
50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016ae974a485f413a2113503eed53cd6c53
10.1109/acssc.2016.7869097