articleTop 1% cited
High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning
Pattern Recognition · 2016 · Vol. 58 · pp. 121–134
Sarah Erfani✉(University of Melbourne)Sutharshan Rajasegarar(University of Melbourne)Shanika Karunasekera(University of Melbourne)Christopher Leckie(Data61)
Anomaly Detection Techniques and ApplicationsNetwork Security and Intrusion DetectionDigital Media Forensic DetectionAnomaly detectionArtificial intelligenceSupport vector machinePattern recognition (psychology)Scale (ratio)Class (philosophy)Computer scienceAnomaly (physics)MathematicsGeography
Funding
- National ICT Australia
- University of Melbourne
- Division of Arctic Sciences
Citations
1,163
FWCI
115.98
field-weighted impact
References
74
Percentile
100%
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Citations per year
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Technometrics · 2004 · 4,697 citations
Hierarchical Grouping to Optimize an Objective Function
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Reducing the Dimensionality of Data with Neural Networks
Science · 2006 · 20,627 citations
Training Products of Experts by Minimizing Contrastive Divergence
Neural Computation · 2002 · 4,959 citations
Estimating the Support of a High-Dimensional Distribution
Neural Computation · 2001 · 5,820 citations
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