Vector Quantization

Vector Quantization is a unsupervised learning method that partitions a set of continuous vectors into discrete prototypes by mapping each input vector to the nearest representative in a finite set of codewords, thereby reducing data dimensionality and redundancy through clustering-based approximation.

2.9K

Publications

184.1K

Citations

5.8K

Authors

1.6K

Institutions

Publications per year

2017–2026

358

Authors

5.8K

  1. RM
    Robert M. Gray

    Stanford University, United States

    67 publications · 14.3K citations

    32

    h-index

  2. AG
    A. Gersho

    University of California, Santa Barbara, United States

    58 publications · 4.1K citations

    32

    h-index

  3. CC
    Chin‐Chen Chang

    Feng Chia University, Taiwan

    26 publications · 1.2K citations

    19

    h-index

  4. TV
    Thomas Villmann

    Hochschule Mittweida, Germany

    33 publications · 1.4K citations

    18

    h-index

  5. BH
    Barbara Hammer

    Bielefeld University, Germany

    31 publications · 1.7K citations

    18

    h-index

1–5 of 5.8K

Venues

  1. IEEE Transactions on Information Theory

    96 publications · 8.3K citations

    50

    h-index

  2. IEEE Transactions on Image Processing

    102 publications · 6.1K citations

    42

    h-index

  3. IEEE Transactions on Communications

    63 publications · 11.1K citations

    37

    h-index

  4. IEEE Transactions on Neural Networks

    54 publications · 6.2K citations

    33

    h-index

  5. IEEE Transactions on Signal Processing

    48 publications · 3.2K citations

    29

    h-index

1–5