Tensor Product Features

Tensor Product Features is a method in machine learning that constructs high-dimensional feature representations by computing the tensor product of individual feature vectors, thereby capturing multiplicative interactions among input variables.

44

Publications

3.4K

Citations

129

Authors

80

Institutions

Publications per year

2017–2026

23

Authors

129

  1. JQ
    Jun Qi

    Georgia Institute of Technology, United States

    2 publications · 45 citations

    2

    h-index

  2. ZH
    Zhu Han

    University of Houston, United States

    2 publications · 1.3K citations

    2

    h-index

  3. PC
    Pin‐Yu Chen

    IBM (United States), United States

    2 publications · 45 citations

    2

    h-index

  4. ML
    Mingsheng Long

    Tsinghua University, China

    2 publications · 1.3K citations

    2

    h-index

  5. JW
    Jianmin Wang

    Tsinghua University, China

    2 publications · 1.3K citations

    2

    h-index

1–5 of 129

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 1.3K citations

    5

    h-index

  2. Information Sciences

    1 publications · 48 citations

    2

    h-index

  3. 1

    h-index

  4. The Journal of Supercomputing

    1 publications · 17 citations

    1

    h-index

  5. Asian Journal of Control

    1 publications · 13 citations

    1

    h-index

1–5