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 language | English |
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Title of host publication | Proceedings - 11th IEEE International Conference on Data Mining, ICDM 2011 |
Pages | 527-536 |
Number of pages | 10 |
DOIs | |
Publication status | Published - 1 Dec 2011 |
Externally published | Yes |
Event | IEEE International Conference on Data Mining 2011 - Vancouver, Canada Duration: 11 Dec 2011 → 14 Dec 2011 Conference number: 11th http://icdm2011.cs.ualberta.ca/ http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6135855 (Conference Proceedings) |
Publication series
Name | Proceedings - IEEE International Conference on Data Mining, ICDM |
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ISSN (Print) | 1550-4786 |
Conference
Conference | IEEE International Conference on Data Mining 2011 |
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Abbreviated title | ICDM 2011 |
Country/Territory | Canada |
City | Vancouver |
Period | 11/12/11 → 14/12/11 |
Internet address |
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