Anomaly Detection from a Tensor Train Perspective
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Abstract (arXiv)
We present a series of algorithms in tensor networks for anomaly detection in datasets, by using data compression in a Tensor Train representation. These algorithms consist of preserving the structure of normal data in compression and deleting the structure of anomalous data. The algorithms can be applied to any tensor network representation. We test the effectiveness of the methods with digits and Olivetti faces datasets and a cybersecurity dataset to determine cyber-attacks.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 18 references that are in the map. First posted on arXiv 23 Sep 2024.
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References · 6
- 2014A Tutorial on Principal Component Analysiscited by 139
- 2023Machine Learning for Anomaly Detection in Particle Physicscited by 29
- 2020Anomaly Detection with Tensor Networkscited by 26
- 2024Supervised Anomaly Detection for Complex Industrial Imagescited by 18
- 2023Comparative Evaluation of Anomaly Detection Methods for Fraud Detection in Online Credit Card Paymentscited by 4
- 2017Statistical Anomaly Detection via Composite Hypothesis Testing for Markov Modelscited by 1
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