2021/06/08 by Martin Bauw, Bauw, Martin, Santiago Velasco-Forero +8
Computer Science · Engineering · Mathematics · #Adversarial Robustness in Machine Learning #Anomaly Detection Techniques and Applications #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (stat.ML) #Network Security and Intrusion Detection #Signal Processing (eess.SP) #cs.AI #eess.SP #electronic engineering #information engineering #stat.ML
paper · pdf · doi:10.48550/arxiv.2106.15307
openalex publication_date 2021/06/08 · arxiv created 2021/07/30 · arxiv updated 2021/08/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.