High-dimensional Anomaly Detection
High-Dimensional Anomaly Detection is a methodological framework within anomaly detection that specifically addresses the identification of outliers in data characterized by a large number of features or dimensions, where traditional distance-based and density-based techniques suffer from the curse of dimensionality.
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33.1K
Citations
1.1K
Authors
453
Institutions
Publications per year
2017–2026
189
Parents
360
Fei Tony Liu, Kai Ming Ting, Zhi‐Hua Zhou · 2008 · 5.6K citations
Algorithms for Mining Distance-Based Outliers in Large Datasets
Edwin M. Knorr, Raymond T. Ng · 1998 · 1.5K citations
Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection
Bo Zong, Song Qi, Martin Renqiang Min et al. · 2018 · 1.1K citations
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Carnegie Mellon University
Pittsburgh, United States
12 h-index
33 publications
2K citations

Ames Research Center
Mountain View, United States
8 h-index
19 publications
2.5K citations
Tsinghua University
Beijing, China
6 h-index
25 publications
929 citations

Monash University
Melbourne, Australia
6 h-index
12 publications
12K citations
University of Illinois Urbana-Champaign
Urbana, United States
5 h-index
8 publications
354 citations
Agency for Science, Technology and Research
Singapore, Singapore
5 h-index
16 publications
2.3K citations

Central South University
Changsha, China
5 h-index
19 publications
358 citations

The University of Melbourne
Melbourne, Australia
5 h-index
19 publications
5.1K citations
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Repository · 14 publications · 913 citations
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13 publications · 892 citations
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TKDE · 7 publications · 932 citations
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6 publications · 171 citations
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TPAMI · 5 publications · 292 citations
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