Detection of cross channel anomalies from multiple data channels
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: anomal...
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE
2011
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/43985 |
| _version_ | 1848756867845586944 |
|---|---|
| author | Pham, DucSon Saha, Budhaditya Phung, Dinh Venkatesh, Svetha |
| author2 | D Cook |
| author_facet | D Cook Pham, DucSon Saha, Budhaditya Phung, Dinh Venkatesh, Svetha |
| author_sort | Pham, DucSon |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T09:19:02Z |
| format | Conference Paper |
| id | curtin-20.500.11937-43985 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:19:02Z |
| publishDate | 2011 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-439852023-01-27T05:52:12Z Detection of cross channel anomalies from multiple data channels Pham, DucSon Saha, Budhaditya Phung, Dinh Venkatesh, Svetha D Cook J Pei W Wang O Zaiane Xindong Wu topic detection Anomaly detection Spectral methods 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. 2011 Conference Paper http://hdl.handle.net/20.500.11937/43985 10.1109/ICDM.2011.51 IEEE restricted |
| spellingShingle | topic detection Anomaly detection Spectral methods Pham, DucSon Saha, Budhaditya Phung, Dinh Venkatesh, Svetha Detection of cross channel anomalies from multiple data channels |
| title | Detection of cross channel anomalies from multiple data channels |
| title_full | Detection of cross channel anomalies from multiple data channels |
| title_fullStr | Detection of cross channel anomalies from multiple data channels |
| title_full_unstemmed | Detection of cross channel anomalies from multiple data channels |
| title_short | Detection of cross channel anomalies from multiple data channels |
| title_sort | detection of cross channel anomalies from multiple data channels |
| topic | topic detection Anomaly detection Spectral methods |
| url | http://hdl.handle.net/20.500.11937/43985 |