Information Loss Analysis

Information Loss Analysis is a methodological framework within data abstraction that systematically quantifies the reduction or distortion of semantic content resulting from data simplification processes, distinguishing it from other abstraction assessments by its focus on measurable fidelity loss in representational integrity.

1.3K

Publications

73.5K

Citations

3.5K

Authors

1.3K

Institutions

Publications per year

2017–2026

613

Authors

3.5K

  1. VT
    Vicenç Torra

    Artificial Intelligence Research Institute, Spain

    5 publications · 73 citations

    5

    h-index

  2. SB
    Steven B. Giddings

    University of California, Santa Barbara, United States

    5 publications · 480 citations

    5

    h-index

  3. FN
    Feiping Nie

    Northwestern Polytechnical University, China

    5 publications · 116 citations

    5

    h-index

  4. MO
    Masafumi Oizumi

    RIKEN Center for Brain Science, Japan

    4 publications · 121 citations

    4

    h-index

  5. HW
    Hua Wang

    Victoria University, Australia

    4 publications · 110 citations

    4

    h-index

1–5 of 3.5K

Venues

  1. arXiv (Cornell University)

    Repository · 58 publications · 3.5K citations

    28

    h-index

  2. IEEE Access

    25 publications · 785 citations

    18

    h-index

  3. Journal of High Energy Physics

    JHEP · 20 publications · 1.1K citations

    18

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 14 publications · 1.3K citations

    14

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 14 publications · 3.6K citations

    14

    h-index

1–5