Summary of Anomaly Detection From a Tensor Train Perspective, by Alejandro Mata Ali et al.
Anomaly Detection from a Tensor Train Perspective
by Alejandro Mata Ali, Aitor Moreno Fdez. de Leceta, Jorge López Rubio
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Emerging Technologies (cs.ET); Information Theory (cs.IT); Quantum Physics (quant-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning researchers have developed novel algorithms for anomaly detection in datasets using tensor networks, specifically Tensor Train representations. These methods compress normal data while deleting anomalies’ structure. The approach can be applied to any tensor network representation. To evaluate effectiveness, the authors tested their methods on digits and Olivetti faces datasets as well as a cybersecurity dataset to identify cyber-attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Anomaly detection is like finding the weird kid in class – it’s all about spotting things that don’t fit in. Researchers have created new ways to find these anomalies using special math tricks called tensor networks. They used a technique called Tensor Train to squeeze out normal patterns while erasing abnormal ones. This method works for any type of data and can even spot cyber-attacks on computers. |
Keywords
» Artificial intelligence » Anomaly detection » Machine learning
