Sparse Vector Modeling

Sparse Vector Modeling is a vector modeling approach that represents data using vectors in which most elements are zero, thereby emphasizing computational efficiency and feature selectivity in high-dimensional spaces.

35

Publications

2.7K

Citations

95

Authors

49

Institutions

Publications per year

2017–2026

8

Authors

95

  1. AP
    Athina P. Petropulu

    Rutgers, The State University of New Jersey, United States

    2 publications · 91 citations

    2

    h-index

  2. JF
    J. Fandino

    Électricité de France (France), France

    2 publications · 151 citations

    2

    h-index

  3. AM
    Alireza Mousavi

    Brunel University of London, United Kingdom

    2 publications · 62 citations

    2

    h-index

  4. JS
    Jean‐Claude Sabonnadière

    Laboratoire de Génie Électrique de Grenoble, France

    2 publications · 151 citations

    2

    h-index

  5. AM
    Arian Maleki

    Columbia University, United States

    2 publications · 62 citations

    2

    h-index

1–5 of 95

Venues

  1. IEEE Transactions on Power Systems

    8 publications · 436 citations

    8

    h-index

  2. IEEE Signal Processing Letters

    3 publications · 61 citations

    3

    h-index

  3. The Annals of Statistics

    2 publications · 62 citations

    2

    h-index

  4. Journal of Chemometrics

    1 publications · 97 citations

    1

    h-index

  5. IEEE Transactions on Vehicular Technology

    1 publications · 34 citations

    1

    h-index

1–5