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Deep Random Projection Outlyingness for Unsupervised Anomaly Detection

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.
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Contributor : Martin Bauw Connect in order to contact the contributor
Submitted on : Monday, July 26, 2021 - 3:46:09 PM
Last modification on : Wednesday, November 17, 2021 - 12:27:19 PM


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  • HAL Id : hal-03203686, version 2
  • ARXIV : 2106.15307


Martin Bauw, Santiago Velasco-Forero, Jesus Angulo, Claude Adnet, Olivier Airiau. Deep Random Projection Outlyingness for Unsupervised Anomaly Detection. 2021. ⟨hal-03203686v2⟩



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