方法证据记录
Ensemble Autoencoder Anomaly Detection
Ensemble Autoencoder Anomaly Detection trains multiple autoencoder neural networks on normal-class data and aggregates their reconstruction errors to produce a robust anomaly score. By combining diverse autoencoders rather than relying on one, the method stabilises outlier rankings and reduces sensitivity to random initialisation or suboptimal architecture choices.
源记录
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Ensemble Autoencoder Anomaly Detection (Multiple Autoencoder Aggregation for Outlier Scoring)
分类方法记录 · ml-model / machine-learning
- Chen, J., Sathe, S., Aggarwal, C., & Turaga, D. (2017). Outlier Detection with Autoencoder Ensembles. In Proceedings of the 2017 SIAM International Conference on Data Mining (SDM), pp. 90–98. SIAM. · URL
- Aggarwal, C. C. (2017). Outlier Analysis (2nd ed., Ch. 3 & 9). Springer. · ISBN 978-3-319-47578-3
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