Tensor Decompositions

Tensor Decompositions is a mathematical framework in numerical linear algebra that generalizes matrix factorization techniques to higher-order tensors by decomposing a tensor into a product of simpler, structured components to reveal latent multilinear relationships.

2.4K

Publications

181K

Citations

4.5K

Authors

1.4K

Institutions

Publications per year

2017–2026

1.2K

Authors

4.5K

  1. LD
    Lieven De Lathauwer

    KU Leuven, Belgium

    63 publications · 12.4K citations

    36

    h-index

  2. AC
    Andrzej Cichocki

    RIKEN Center for Brain Science, Japan

    69 publications · 6.1K citations

    34

    h-index

  3. LQ
    Liqun Qi

    Hong Kong Polytechnic University, Hong Kong SAR China

    37 publications · 2.3K citations

    26

    h-index

  4. XZ
    Xi-Le Zhao

    University of Electronic Science and Technology of China, China

    31 publications · 2.3K citations

    25

    h-index

  5. QZ
    Qibin Zhao

    RIKEN Center for Advanced Intelligence Project, Japan

    37 publications · 3.3K citations

    24

    h-index

1–5 of 4.5K

Venues

  1. SIAM Journal on Matrix Analysis and Applications

    88 publications · 15K citations

    47

    h-index

  2. IEEE Transactions on Signal Processing

    88 publications · 7.7K citations

    42

    h-index

  3. 35

    h-index

  4. arXiv (Cornell University)

    Repository · 155 publications · 5.9K citations

    35

    h-index

  5. Linear Algebra and its Applications

    56 publications · 4.3K citations

    31

    h-index

1–5