A fuzzy detection approach to high-dimensional anomalies
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00530-024-01343-7.pdf
Reference47 articles.
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3. Wang, H., Pang, G., Shen, C., et al.: Unsupervised representation learning by predicting random distances. In: Twenty-Ninth International Joint Conference on Artificial Intelligence Main track, pp. 2950–2956 (2020)
4. Li, Y., Peng, X., Zhang, J., et al.: DCT-GAN: dilated convolutional transformer-based GAN for time series anomaly detection. IEEE Trans. Knowl. Data Eng. 35(4), 3632–3644 (2023)
5. Kong, F., Li, J., Jiang, B., et al.: Integrated generative model for industrial anomaly detection via bidirectional LSTM and attention mechanism. IEEE Trans. Industr. Inf. 19(1), 541–550 (2023)
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