Detection of cross-channel anomalies from multiple data channels

Duc Son Pham, Budhaditya Saha, Dinh Q. Phung, Svetha Venkatesh

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

2 Citations (Scopus)

Abstract

We identify and formulate a novel problem: cross-channel anomaly detection from multiple data channels. Cross channel anomalies are common amongst the individual channel anomalies, and are often portent of significant events. Using spectral approaches, we propose a two-stage detection method: anomaly detection at a single-channel level, followed by the detection of cross-channel anomalies from the amalgamation of single channel anomalies. Our mathematical analysis shows that our method is likely to reduce the false alarm rate. We demonstrate our method in two applications: document understanding with multiple text corpora, and detection of repeated anomalies in video surveillance. The experimental results consistently demonstrate the superior performance of our method compared with related state-of-art methods, including the one-class SVM and principal component pursuit. In addition, our framework can be deployed in a decentralized manner, lending itself for large scale data stream analysis.

Original languageEnglish
Title of host publicationProceedings - 11th IEEE International Conference on Data Mining, ICDM 2011
Pages527-536
Number of pages10
DOIs
Publication statusPublished - 1 Dec 2011
Externally publishedYes
Event11th IEEE International Conference on Data Mining, ICDM 2011 - Vancouver, Canada
Duration: 11 Dec 201114 Dec 2011

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Conference

Conference11th IEEE International Conference on Data Mining, ICDM 2011
CountryCanada
CityVancouver
Period11/12/1114/12/11

Cite this

Pham, D. S., Saha, B., Phung, D. Q., & Venkatesh, S. (2011). Detection of cross-channel anomalies from multiple data channels. In Proceedings - 11th IEEE International Conference on Data Mining, ICDM 2011 (pp. 527-536). (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDM.2011.51