Anomaly Detection from a Tensor Train Perspective
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866915974526009344 |
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| author | Ali, Alejandro Mata de Leceta, Aitor Moreno Fdez. Rubio, Jorge López |
| author_facet | Ali, Alejandro Mata de Leceta, Aitor Moreno Fdez. Rubio, Jorge López |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_15030 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Anomaly Detection from a Tensor Train Perspective Ali, Alejandro Mata de Leceta, Aitor Moreno Fdez. Rubio, Jorge López Machine Learning Cryptography and Security Emerging Technologies Information Theory Quantum Physics 94A15, 81P68 G.1.3; H.3.3; K.6.5 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. |
| title | Anomaly Detection from a Tensor Train Perspective |
| topic | Machine Learning Cryptography and Security Emerging Technologies Information Theory Quantum Physics 94A15, 81P68 G.1.3; H.3.3; K.6.5 |
| url | https://arxiv.org/abs/2409.15030 |