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Anomaly Detection from a Tensor Train Perspective

23 September 2024
Alejandro Mata Ali
Aitor Moreno Fdez. de Leceta
Jorge López Rubio
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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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