Adaptive anomaly detection with kernel eigenspace splitting and merging

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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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
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
Published Online:15 May 2014

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