The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Convolutional Neural Networks for Unsupervised Anomaly Detection in Text Data
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In the citing paper
“Moreover, CNNs have also been shown to be useful for extracting complex hidden features in sequential data [74].”
From A Survey on Explainable Anomaly Detection (2024) · cites this paper as [74]
Citations
Cites
(2014) · Cited by 33,419
Convolutional Neural Networks for Sentence Classification
Kim, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1181 · Cited by 14,831
Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687
One-class svms for document classification
Manevitz, L. M., & Yousef, M. (2002) · doi:10.5555/944790.944808 · Cited by 1,234
Outlier Detection Using Replicator Neural Networks
Hawkins, S., He, H., Williams, G., & Baxter, R. A. (2002) · Lecture notes in computer science · doi:10.1007/3-540-46145-0_17 · Cited by 750
Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks
Lee, J. Y., & Dernoncourt, F. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1062 · Cited by 505
Outlier Detection for Text Data
Kannan, R., Woo, H., Aggarwal, C. C., & Park, H. (2017) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611974973.55 · Cited by 56
Probabilistic Novelty Detection With Support Vector Machines
Clifton, L., Clifton, D. A., Zhang, Y., Watkinson, P., Tarassenko, L., & Yin, H. (2014) · IEEE Transactions on Reliability · doi:10.1109/tr.2014.2315911 · Cited by 50
Outlier Detection for Text Data : An Extended VersionPreprint
Kannan, R., Woo, H., Aggarwal, C. C., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.01325 · Cited by 17
Converged Algorithms for Orthogonal Nonnegative Matrix FactorizationsPreprint
Mirzal, A. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1010.5290 · Cited by 8
Cited by
Sorted by citations
A Survey on Explainable Anomaly Detection
Li, Z., Zhu, Y., & Van Leeuwen, M. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3609333 · Cited by 139
“Accordingly, a variety of CNN architectures have been proposed, including LeNet, AlexNet, GoogleNet, VGGNet, Inception V4, ResNet, etc.”
BackgroundMethodInfluential
Metadata
- Authors
- Gorokhov, O., Petrovskiy, M., & Mashechkin, I.
- Affiliations
- Lomonosov Moscow State University (RU)
- Corresponding author
- Petrovskiy, Mikhail
- ORCID
- Petrovskiy, Mikhail
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2017
- Pages
- 500-507
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2764198198
- Cited by
- 34
- References
- 13
- Citations by year
- 2025: 3 · 2024: 3 · 2023: 8 · 2022: 3 · 2021: 3 · 2020: 2 · 2019: 3 · 2018: 9
- FWCI
- 1.75
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- unsupervised anomaly detection, text data, convolutional neural networks, outlier detection, Tikhonov regularization, Enron email dataset, spam detection
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Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: Convolutional Neural Networks for Unsupervised Anomaly Detection in Text Data. https://app.cochat.ai/papers/10.1007/978-3-319-68935-7_54
- The paper
- Gorokhov, O., Petrovskiy, M., & Mashechkin, I. (2017). Convolutional Neural Networks for Unsupervised Anomaly Detection in Text Data. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-319-68935-7_54
