Inference models such as the confabulation network are particularly useful in anomaly detection applications because they allow introspection to the decision process. However, building such network model always requires expert knowledge. In this paper, we present a self-structuring technique that learns the structure of a confabulation network from unlabeled data. Without any assumption of the distribution of data, we leverage the mutual information between features to learn a succinct network configuration, and enable fast incremental learning to refine the knowledge bases from continuous data streams. Compared to several existing anomaly detection methods, the proposed approach provides higher detection performance and excellent reasoning capability. We also exploit the massive parallelism that is inherent to the inference model and accelerate the detection process using GPUs. Experimental results show significant speedups and the potential to be applied to real-time applications with high-volume data streams.
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