Sublinear-time Compression

Sublinear-Time Compression is a data compression framework that belongs to the class of universal coding methods, distinguished by its ability to achieve compression in time sublinear relative to the input size through adaptive sampling and probabilistic modeling.

5

Publications

297

Citations

9

Authors

11

Institutions

Publications per year

2017–2026

0

Authors

9

  1. CG
    Craig G. Nevill-Manning

    University of Waikato, New Zealand

    1 publications · 107 citations

    1

    h-index

  2. GD
    Gary Davies

    University of Manchester, United Kingdom

    1 publications · 40 citations

    1

    h-index

  3. KS
    Kunihiko Sadakane

    Osaka Kyoiku University, Japan

    1 publications · 23 citations

    1

    h-index

  4. WS
    Wing‐Kin Sung

    National University of Singapore, Singapore

    1 publications · 23 citations

    1

    h-index

  5. JJ
    Jesper Jansson

    National University of Singapore, Singapore

    1 publications · 23 citations

    1

    h-index

1–5 of 9

Venues

  1. European Journal of Marketing

    EJM · 1 publications · 40 citations

    1

    h-index

  2. Algorithmica

    1 publications · 23 citations

    1

    h-index

  3. Very Large Data Bases

    Conference · 1 publications · 12 citations

    1

    h-index

  4. Journal of Classification

    1 publications · 115 citations

    1

    h-index

1–4