Kernel-based Change-point Detection

Kernel-Based Change-Point Detection is a change-point detection method that employs kernel functions to map data into a higher-dimensional feature space, enabling the identification of distributional changes through differences in reproducing kernel Hilbert space embeddings.

33

Publications

1.8K

Citations

80

Authors

54

Institutions

Publications per year

2017–2026

13

Authors

80

  1. ZH
    Zaïd Harchaoui

    Institut national de recherche en sciences et technologies du numérique, France

    4 publications · 369 citations

    4

    h-index

  2. AA
    Allan Aasbjerg Nielsen

    Technical University of Denmark, Denmark

    3 publications · 207 citations

    3

    h-index

  3. SA
    Sylvain Arlot

    École Normale Supérieure - PSL, France

    3 publications · 232 citations

    3

    h-index

  4. AC
    Alain Célisse

    Laboratoire Paul Painlevé, France

    3 publications · 251 citations

    3

    h-index

  5. HD
    Hanjun Dai

    Georgia Institute of Technology, United States

    2 publications · 92 citations

    2

    h-index

1–5 of 80

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 244 citations

    4

    h-index

  2. Sequential Analysis

    2 publications · 55 citations

    2

    h-index

  3. Electronic Journal of Statistics

    2 publications · 71 citations

    2

    h-index

  4. Pattern Recognition

    1 publications · 22 citations

    1

    h-index

  5. International Journal of Production Research

    1 publications · 21 citations

    1

    h-index

1–5