Vector quantization: Difference between revisions

Revision as of 03:27, 29 November 2022

Revision as of 21:26, 18 April 2023

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# 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

#eee3 Set <math>s_i</math> to zero

# Repeat

It is desirable to use a cooling schedule to produce convergence: see [[Simulated annealing]]. Another (simpler)l method is [[Linde–Buzo–GrayeLinde–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.