Sparse Coding

Sparse Coding is a representation learning method in unsupervised learning that characterizes a data point as a sparse linear combination of basis elements from an overcomplete dictionary, where the sparsity constraint distinguishes it from other factorization or encoding techniques.

3K

Publications

278.5K

Citations

6.7K

Authors

1.6K

Institutions

Publications per year

2017–2026

979

Authors

6.7K

  1. ME
    Michael Elad

    Technion – Israel Institute of Technology, Israel

    47 publications · 24.8K citations

    31

    h-index

  2. SY
    Shuicheng Yan

    National University of Singapore, Singapore

    38 publications · 5.5K citations

    26

    h-index

  3. LZ
    Lei Zhang

    Hong Kong Polytechnic University, Hong Kong SAR China

    25 publications · 9K citations

    23

    h-index

  4. TS
    Thomas S. Huang

    University of Illinois Urbana-Champaign, United States

    25 publications · 16.3K citations

    23

    h-index

  5. GS
    Guillermo Sapiro

    University of Minnesota, United States

    24 publications · 12.7K citations

    21

    h-index

1–5 of 6.7K

Venues

  1. IEEE Transactions on Image Processing

    121 publications · 32.5K citations

    52

    h-index

  2. IEEE Transactions on Signal Processing

    85 publications · 22.7K citations

    48

    h-index

  3. arXiv (Cornell University)

    Repository · 107 publications · 4.4K citations

    35

    h-index

  4. 35

    h-index

  5. Neurocomputing

    96 publications · 3.4K citations

    33

    h-index

1–5