Anomaly Detection in Vertically Partitioned Data by Distributed Core Vector Machines
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Observations of physical processes suffer from instrument malfunction and noise and demand data cleansing. However, rare events are not to be excluded from modeling, since they can be the most interesting findings. Often, sensors collect features at different sites, so that only a subset is present (vertically distributed data). Transferring all data or a sample to a single location is impossible in many real-world applications due to restricted bandwidth of communication. Finding interesting abnormalities thus requires efficient methods of distributed anomaly detection. We propose a new algorithm for anomaly detection on vertically distributed data. It aggregates the data directly at the local storage nodes using RBF kernels. Only a fraction of the data is communicated to a central node. Through extensive empirical evaluation on controlled datasets, we demonstrate that our method is an order of magnitude more communication efficient than state of the art methods, achieving a comparable accuracy.
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