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.

360

Publications

33.1K

Citations

1.1K

Authors

453

Institutions

Publications per year

2017–2026

189

Authors

1.1K

  1. CL
    Christopher Leckie

    The University of Melbourne, Australia

    5 publications · 1.2K citations

    5

    h-index

  2. KM
    Kai Ming Ting

    Monash University, Australia

    5 publications · 5.9K citations

    5

    h-index

  3. CF
    Christos Faloutsos

    Carnegie Mellon University, United States

    5 publications · 255 citations

    5

    h-index

  4. CF
    Chuan-Sheng Foo

    Institute for Infocomm Research, Singapore

    4 publications · 918 citations

    4

    h-index

  5. CC
    Chein‐I Chang

    University of Maryland, Baltimore County, United States

    4 publications · 213 citations

    4

    h-index

1–5 of 1.1K

Venues

  1. arXiv (Cornell University)

    Repository · 14 publications · 913 citations

    14

    h-index

  2. IEEE Access

    13 publications · 892 citations

    11

    h-index

  3. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 7 publications · 932 citations

    7

    h-index

  4. 6

    h-index

  5. 5

    h-index

1–5