Deep Random Projection Outlyingness for Unsupervised Anomaly Detection
This work addresses anomaly detection for data with complex distributions, but it is incremental as it builds on existing random projection techniques.
The paper tackled the problem of unsupervised anomaly detection by modifying a random projection outlyingness measure and integrating it with a neural network to handle multimodal normality, achieving performance comparable to a state-of-the-art method on datasets like MNIST, Fashion-MNIST, and CIFAR-10.
Random projection is a common technique for designing algorithms in a variety of areas, including information retrieval, compressive sensing and measuring of outlyingness. In this work, the original random projection outlyingness measure is modified and associated with a neural network to obtain an unsupervised anomaly detection method able to handle multimodal normality. Theoretical and experimental arguments are presented to justify the choice of the anomaly score estimator. The performance of the proposed neural network approach is comparable to a state-of-the-art anomaly detection method. Experiments conducted on the MNIST, Fashion-MNIST and CIFAR-10 datasets show the relevance of the proposed approach.