The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection
Open accessAbstract
Learning expressive low-dimensional representations of ultrahigh-dimensional data, e.g., data with thousands/millions of features, has been a major way to enable learning methods to address the curse of dimensionality. However, existing unsupervised representation learning methods mainly focus on preserving the data regularity information and learning the representations independently of subsequent outlier detection methods, which can result in suboptimal and unstable performance of detecting irregularities (i.e., outliers).
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Citations
Cites
ADADELTA: An Adaptive Learning Rate Method
Zeiler, M. D. (2012) · arXiv · doi:10.48550/arxiv.1212.5701 · Cited by 5,322
A survey on unsupervised outlier detection in high‐dimensional numerical data
Zimek, A., Schubert, E., & Kriegel, H. (2012) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11161 · Cited by 864 · cited 1×
“Such ultrahigh-dimensional data presents significant challenges to existing outlier detection methods due to the curse of dimensionality [38].”
Background
Cited by
Deep Learning for Anomaly Detection: A Review
Pang, G., Shen, C., Cao, L., & Hengel, A. V. D. (2022) · ACM Computing Surveys · doi:10.1145/3439950 · Cited by 2,837
Self-citationProgress in Outlier Detection Techniques: A Survey
Wang, H., Bah, M. J., & Hammad, M. (2019) · IEEE Access · doi:10.1109/access.2019.2932769 · Cited by 523
Viral pneumonia screening on chest X-rays using confidence-aware anomaly detection
Zhang, J., Xie, Y., Pang, G., Liao, Z., Verjans, J., Li, W., Sun, Z., He, J., Li, Y., Shen, C., et al. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3040950 · Cited by 389
Self-citationOutlier Detection: Methods, Models, and Classification
Boukerche, A., Zheng, L., & Alfandi, O. (2021) · ACM Computing Surveys · doi:10.1145/3381028 · Cited by 334
Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection
Ding, C., Pang, G., & Shen, C. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00724 · Cited by 197
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Metadata
- Authors
- Pang, G., Cao, L., Chen, L., & Liu, H.
- Venue
- Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
- Publisher
- ACM
- Published
- 2018-07-19
- Pages
- 2041-2050
- OpenAlex
- W2807955733
- Cited by
- 223
- References
- 2
- FWCI
- 7.91
- Type
- conference paper
- Language
- en
- Open access
- green
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- Pang, G., Cao, L., Chen, L., & Liu, H. (2018). Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. https://doi.org/10.1145/3219819.3220042
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