What does vector quantization mean?

Definitions for vector quantization
vec·tor quan·ti·za·tion

This dictionary definitions page includes all the possible meanings, example usage and translations of the word vector quantization.

Wikipedia

  1. Vector quantization

    Vector quantization (VQ) is a classical quantization technique from signal processing that allows the modeling of probability density functions by the distribution of prototype vectors. It was originally used for data compression. It works by dividing a large set of points (vectors) into groups having approximately the same number of points closest to them. Each group is represented by its centroid point, as in k-means and some other clustering algorithms. The density matching property of vector quantization is powerful, especially for identifying the density of large and high-dimensional data. Since data points are represented by the index of their closest centroid, commonly occurring data have low error, and rare data high error. This is why VQ is suitable for lossy data compression. It can also be used for lossy data correction and density estimation. Vector quantization is based on the competitive learning paradigm, so it is closely related to the self-organizing map model and to sparse coding models used in deep learning algorithms such as autoencoder.

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Numerology

  1. Chaldean Numerology

    The numerical value of vector quantization in Chaldean Numerology is: 7

  2. Pythagorean Numerology

    The numerical value of vector quantization in Pythagorean Numerology is: 7

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"vector quantization." Definitions.net. STANDS4 LLC, 2024. Web. 21 Apr. 2024. <https://www.definitions.net/definition/vector+quantization>.

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