High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning
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OC-SVM achieved lower performance in high-dimensional datasets.
“This behavior was analyzed in [87], where OC-SVM achieved a lower performance in high-dimensional datasets.”
From Deep Learning Approaches for Detecting Freezing of Gait in Parkinson’s Disease Patients through On-Body Acceleration Sensors (2020) · cites this paper as [87]
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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
- bronze
- doi.org
- Licence
- 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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