xAdaptive Anomaly Detection with Kernel Eigenspace Splitting and
Merging
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 nonstationary, 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.