Subspace Anomaly Detection

Subspace Anomaly Detection is a method in anomaly detection that identifies outliers within specific low-dimensional subspaces of a high-dimensional data space, where anomalies deviate significantly from normal patterns only with respect to a subset of dimensions rather than the entire space.

76

Publications

5.7K

Citations

232

Authors

108

Institutions

Publications per year

2017–2026

35

Authors

232

  1. CD
    Christophe Diot

    Sprint (United States), United States

    5 publications · 2.2K citations

    5

    h-index

  2. MC
    Mark Crovella

    Boston University, United States

    4 publications · 2.1K citations

    4

    h-index

  3. AL
    Anukool Lakhina

    Intel (United Kingdom), United Kingdom

    4 publications · 2.1K citations

    4

    h-index

  4. QD
    Qian Du

    Mississippi State University, United States

    3 publications · 150 citations

    3

    h-index

  5. NM
    Nasser M. Nasrabadi

    DEVCOM Army Research Laboratory, United States

    3 publications · 82 citations

    3

    h-index

1–5 of 232

Venues

  1. 6

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  2. 3

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  3. 3

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  4. International Journal of Remote Sensing

    2 publications · 66 citations

    2

    h-index

  5. IEEE Geoscience and Remote Sensing Letters

    GRSL · 2 publications · 67 citations

    2

    h-index

1–5