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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]].
== Training ==
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