Editing Vector quantization (section)

{{short description|Classical quantization technique from signal processing}}
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'''Vector quantization''' ('''VQ''') is a classical [[Quantization (signal processing)|quantization]] technique from [[signal processing]] that allows the modeling of [[probability density functions]] by the distribution of prototype vectors. Developed in the early 1980s by [[Robert M. Gray]], it was originally used for [[data compression]].  It works by dividing a large set of points ([[coordinate vector|vector]]s) 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 [[Cluster analysis|clustering]] algorithms. In simpler terms, vector quantization chooses a set of points to represent a larger set of points.

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]].