Robust Feature Representation

Robust Feature Representation is a type of feature representation that maintains discriminative quality and structural consistency under variations, noise, and transformations in data, thereby enabling reliable performance across diverse and adverse conditions.

1.1K

Publications

75.2K

Citations

3.6K

Authors

1K

Institutions

Publications per year

2017–2026

603

Authors

3.6K

  1. JY
    Jian Yang

    Nanjing University of Science and Technology, China

    8 publications · 358 citations

    8

    h-index

  2. JT
    Jin Tang

    Anhui University, China

    8 publications · 601 citations

    8

    h-index

  3. ZZ
    Zhao Zhang

    Hefei University of Technology, China

    8 publications · 482 citations

    8

    h-index

  4. DZ
    David Zhang

    Hong Kong Polytechnic University, Hong Kong SAR China

    7 publications · 439 citations

    7

    h-index

  5. ZZ
    Zheng-Jun Zha

    University of Science and Technology of China, China

    7 publications · 330 citations

    7

    h-index

1–5 of 3.6K

Venues

  1. arXiv (Cornell University)

    Repository · 55 publications · 3.2K citations

    26

    h-index

  2. IEEE Transactions on Image Processing

    24 publications · 2.3K citations

    23

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 23 publications · 1.9K citations

    19

    h-index

  4. Pattern Recognition

    25 publications · 1.3K citations

    19

    h-index

  5. 18

    h-index

1–5