Deep learning for anomaly detection
Anomaly detection methods are devoted to target detection schemes in which no priori information about the spectra of the targets of interest is available. This paper research on the 4 various types of anomaly detection machine learning anomaly models, namely Local Outlier Factor (LOF), Isolation...
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| 其他作者: | |
| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
Nanyang Technological University
2022
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/157429 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | Anomaly detection methods are devoted to target detection schemes in which no priori
information about the spectra of the targets of interest is available. This paper research on
the 4 various types of anomaly detection machine learning anomaly models, namely Local
Outlier Factor (LOF), Isolation Forest, One-Class Support Vector Machine (SVM), and
Robust Covariance. Additionally, this paper shows the various steps in the implementation
anomaly models and studies the effectiveness of each model in analysing an industrialized
Multivariate Time-Series dataset. |
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