Anomaly Detection from a Tensor Train Perspective

Alejandro Mata Ali, Aitor Moreno Fdez. de Leceta, Jorge López Rubio
9/23/2024
cs.LGcs.CRcs.ET

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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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Cite this paper

@article{ali2024anomaly,
  title  = {Anomaly Detection from a Tensor Train Perspective},
  author = {Alejandro Mata Ali and Aitor Moreno Fdez. de Leceta and Jorge López Rubio},
  year   = {2024},
  eprint = {2409.15030},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2409.15030v1}
}

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