  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.
