Deep Random Projection Outlyingness for Unsupervised Anomaly Detection
Signal Processing
2021-08-02 v2 Artificial Intelligence
Machine Learning
Abstract
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.
Cite
@article{arxiv.2106.15307,
title = {Deep Random Projection Outlyingness for Unsupervised Anomaly Detection},
author = {Martin Bauw and Santiago Velasco-Forero and Jesus Angulo and Claude Adnet and Olivier Airiau},
journal= {arXiv preprint arXiv:2106.15307},
year = {2021}
}