Cited by 1,162Anomaly Detection Techniques and ApplicationsAdversarial Robustness in Machine LearningSoftware System Performance and Reliability

High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning

Open access
Erfani, Sarah M., Rajasegarar, Sutharshan, Karunasekera, Shanika, Leckie, Christopher·2016·Pattern Recognition

Integrity signals

Registry

Crossref · 10.1016/j.patcog.2016.03.028

Cites

66 of 66 references verified0 citation errors · 0 not checkable

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Authors
Erfani, S. M., Rajasegarar, S., Karunasekera, S., & Leckie, C.
Affiliations
The University of Melbourne (AU)
Corresponding author
Erfani, Sarah M.
Venue
Pattern Recognition
Publisher
Elsevier BV
Published
2016-10
Volume
58
Pages
121-134
ISSN
0031-3203 (ISSN-L)
OpenAlex
W2340896621
Cited by
1,162
References
66
Citations by year
2026: 43 · 2025: 106 · 2024: 128 · 2023: 143 · 2022: 159 · 2021: 184 · 2020: 149 · 2019: 121 · 2018: 72 · 2017: 46 · 2016: 11
FWCI
68.63
Type
journal article
Language
en
Open access
Locations
  • Pattern Recognition (acceptedVersion)
  • Figshare (submittedVersion, cc-by)
Funders
  • NICTA
  • ARC (LP120100529, LE120100129)
  • University of Melbourne Early Career Research
  • EU FP7
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
  • Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
Keywords
anomaly detection, high-dimensional data, one-class support vector machine, deep belief network, unsupervised feature extraction, hybrid model, linear kernel, curse of dimensionality, deep autoencoder, scalability

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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