Vector Quantization Algorithm
A Vector Quantization Algorithm is a Quantization Algorithm that ...
- AKA: Vector Coding.
- See: Autoencoder, Quantization (Signal Processing), Signal Processing, Data Compression, Coordinate Vector, Lossy Data Compression, Density Estimation, Self-Organizing Map, Sparse Coding.
References
2015
- (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/vector_quantization Retrieved:2015-2-14.
- Vector quantization (VQ) is a classical quantization technique from signal processing which 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-dimensioned 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.
- Vector quantization (VQ) is a classical quantization technique from signal processing which 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.