Anomaly Detection from a Tensor Train Perspective

• URL: http://arxiv.org/abs/2409.15030v1
• Date: Mon, 23 Sep 2024 13:55:58 GMT
• Title: Anomaly Detection from a Tensor Train Perspective
• Abstract summary: We present a series of algorithms in tensor networks for anomaly detection in datasets. These algorithms consist of preserving the structure of normal data in compression and deleting the structure of anomalous data. We test the effectiveness of the methods with digits and Olivetti faces datasets and a cybersecurity dataset to determine cyber-attacks.
• Score: 44.99833362998488
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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