Editing Vector quantization (section)

== Training ==
The simplest training algorithm for vector quantization is:<ref>{{cite book|title=An Introduction to Natural Computation|author=Dana H. Ballard|date=2000|publisher=MIT Press|isbn=978-0-262-02420-4|page=189}}
</ref>
# Pick a sample point at random
# Move the nearest quantization vector centroid towards this sample point, by a small fraction of the distance
# Repeat

A more sophisticated algorithm reduces the bias in the density matching estimation, and ensures that all points are used, by including an extra sensitivity parameter {{Citation needed|date=November 2016}}:
# Increase each centroid's sensitivity <math>s_i</math> by a small amount
# Pick a sample point <math>P</math> at random
# For each quantization vector centroid <math>c_i</math>, let <math>d(P, c_i)</math> denote the distance of <math>P</math> and <math>c_i</math>
# Find the centroid <math>c_i</math> for which <math>d(P, c_i) - s_i</math> is the smallest
# Move <math>c_i</math> towards <math>P</math> by a small fraction of the distance
# Set <math>s_i</math> to zero
# Repeat

It is desirable to use a cooling schedule to produce convergence: see [[Simulated annealing]].  Another (simpler) method is [[Linde–Buzo–Gray algorithm|LBG]] which is based on [[K-means clustering|K-Means]].

The algorithm can be iteratively updated with 'live' data, rather than by picking random points from a data set, but this will introduce some bias if the data are temporally correlated over many samples.