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Preprints, Working Papers, ... Year : 2021

Deep Random Projection Outlyingness for Unsupervised Anomaly Detection

Jesus Angulo
Claude Adnet
  • Function : Author
Olivier Airiau
  • Function : Author

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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Dates and versions

hal-03203686 , version 1 (07-06-2021)
hal-03203686 , version 2 (26-07-2021)

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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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