KAUST Grant NumberOSR-2015-Sensors-2700
Online Publication Date2017-03-06
Print Publication Date2016-11
Permanent link to this recordhttp://hdl.handle.net/10754/623599
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AbstractIn 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.
CitationChepuri 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.
SponsorsThis work was supported by the KAUST-MIT-TUD consortium grant OSR-2015-Sensors-2700.
Conference/Event name50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016