A Subspace Projection Approach to Autoencoder-based Anomaly Detection
This work addresses anomaly detection for data analysis applications, offering an incremental improvement over existing autoencoder methods.
The paper tackles the challenge of balancing high-fidelity reconstruction and limited generalization in autoencoder-based anomaly detection by proposing HFR-AE, a framework that projects inputs into a subspace to enhance error gaps, resulting in up to 13.4% improvement in AUROC.
Autoencoder (AE) is a neural network (NN) architecture that is trained to reconstruct an input at its output. By measuring the reconstruction errors of new input samples, AE can detect anomalous samples deviated from the trained data distribution. The key to success is to achieve high-fidelity reconstruction (HFR) while restricting AE's capability of generalization beyond training data, which should be balanced commonly via iterative re-training. Alternatively, we propose a novel framework of AE-based anomaly detection, coined HFR-AE, by projecting new inputs into a subspace wherein the trained AE achieves HFR, thereby increasing the gap between normal and anomalous sample reconstruction errors. Simulation results corroborate that HFR-AE improves the area under receiver operating characteristic curve (AUROC) under different AE architectures and settings by up to 13.4% compared to Vanilla AE-based anomaly detection.