Autoencoder-based Imputation

Autoencoder-Based Imputation is a missing data imputation method that leverages an autoencoder neural network architecture to learn low-dimensional representations of complete data and reconstruct missing values through encoding and decoding processes.

73

Publications

4.2K

Citations

310

Authors

157

Institutions

Publications per year

2017–2026

64

Authors

310

  1. JS
    Jiangwen Sun

    University of Connecticut, United States

    2 publications · 151 citations

    2

    h-index

  2. DA
    Dimitris Askounis

    National Technical University of Athens, Greece

    2 publications · 84 citations

    2

    h-index

  3. KS
    Karan Sareen

    Indian Institute of Technology Delhi, India

    2 publications · 115 citations

    2

    h-index

  4. TM
    Tshilidzi Marwala

    University of the Witwatersrand, South Africa

    2 publications · 30 citations

    2

    h-index

  5. CY
    Chunhua Yang

    Central South University, China

    2 publications · 148 citations

    2

    h-index

1–5 of 310

Venues

  1. IEEE Access

    4 publications · 256 citations

    4

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  2. Measurement

    3 publications · 180 citations

    3

    h-index

  3. arXiv (Cornell University)

    Repository · 3 publications · 515 citations

    3

    h-index

  4. Energy

    2 publications · 115 citations

    2

    h-index

  5. 2

    h-index

1–5