O'Reilly, C., Gluhak, A. and Imran, M. A. (2015) Adaptive anomaly detection with kernel eigenspace splitting and merging. IEEE Transactions on Knowledge and Data Engineering, 27(1), pp. 3-16. (doi: 10.1109/TKDE.2014.2324594)
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Abstract
Kernel principal component analysis and the reconstruction error is an effective anomaly detection technique for non-linear data sets. In an environment where a phenomenon is generating data that is non-stationary, anomaly detection requires a recomputation of the kernel eigenspace in order to represent the current data distribution. Recomputation is a computationally complex operation and reducing computational complexity is therefore a key challenge. In this paper, we propose an algorithm that is able to accurately remove data from a kernel eigenspace without performing a batch recomputation. Coupled with a kernel eigenspace update, we demonstrate that our technique is able to remove and add data to a kernel eigenspace more accurately than existing techniques. An adaptive version determines an appropriately sized sliding window of data and when a model update is necessary. Experimental evaluations on both synthetic and real-world data sets demonstrate the superior performance of the proposed approach in comparison to alternative incremental KPCA approaches and alternative anomaly detection techniques.
Item Type: | Articles |
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Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Imran, Professor Muhammad |
Authors: | O'Reilly, C., Gluhak, A., and Imran, M. A. |
College/School: | College of Science and Engineering > School of Engineering |
Journal Name: | IEEE Transactions on Knowledge and Data Engineering |
Publisher: | IEEE |
ISSN: | 1041-4347 |
Published Online: | 15 May 2014 |
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