Nonparametric detection of an anomalous disk over a two-dimensional lattice network

Shaofeng Zou, Yingbin Liang, H. Vincent Poor

Research output: Chapter in Book/Entry/PoemConference contribution

4 Scopus citations

Abstract

Nonparametric detection of existence of an anomalous disk over a lattice network is investigated. If an anomalous disk exists, then all nodes belonging to the disk observe samples generated by a distribution q, whereas all other nodes observe samples generated by a distribution p that is distinct from q. If there does not exist an anomalous disk, then all nodes receive samples generated by p. The distributions p and q are arbitrary and unknown. The goal is to design statistically consistent test as the network size becomes asymptotically large. A kernel-based test is proposed based on maximum mean discrepancy (MMD) which measures the distance between mean embeddings of distributions into a reproducing kernel Hilbert space (RKHS). A sufficient condition on the minimum size of candidate anomalous disks is characterized in order to guarantee the consistency of the proposed test. A necessary condition that any universally consistent test must satisfy is further derived. Comparison of sufficient and necessary conditions yields that the proposed test is order-level optimal.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2394-2398
Number of pages5
ISBN (Electronic)9781479999880
DOIs
StatePublished - May 18 2016
Event41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China
Duration: Mar 20 2016Mar 25 2016

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2016-May
ISSN (Print)1520-6149

Other

Other41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Country/TerritoryChina
CityShanghai
Period3/20/163/25/16

Keywords

  • Consistency
  • maximum mean discrepancy
  • nonparametric detection
  • reproducing kernel Hilbert space

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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