Linde–Buzo–Gray algorithm: Difference between revisions

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The '''Linde–Buzo–Gray algorithm''' (named after its creators Yoseph Linde, Andrés Buzo and [[Robert M. Gray]], who designed it in 1980)<ref>{{Cite journal| doi = 10.1109/TCOM.1980.1094577| issn = 0090-6778| volume = 28| issue = 1| pages = 84–95| last1 = Linde| first1 = Y.| last2 = Buzo| first2 = A.| last3 = Gray| first3 = R.| title = An Algorithm for Vector Quantizer Design| journal = IEEE Transactions on Communications| date = 1980| s2cid = 18530691}}</ref> is an [[Iterative method|iterative]] [[vector quantization]] algorithm to improve a small set of vectors (codebook) to represent a larger set of vectors (training set), such that it will be [[Local optimum|locally optimal]]. It combines [[Lloyd's Algorithm]] with a splitting technique in which larger codebooks are built from smaller codebooks by splitting each code vector in two. The core idea of the algorithm is that by splitting the codebook such that all code vectors from the previous codebook are present, the new codebook must be as good as the previous one or better. <ref name=gray1992>{{Cite book| edition = 1| publisher = Springer| isbn = 978-1-4613-6612-6| last1 = Gray| first1 = R.| last2 = Gersho| first2 = A.| title = Vector Quantization and Signal Compression| date = 1992| doi = 10.1007/978-1-4615-3626-0| url = https://doi.org/10.1007/978-1-4615-3626-0}}</ref>{{rp|361-362}}

The '''Linde–Buzo–Gray algorithm''' (introduced by Yoseph Linde, Andrés Buzo and [[Robert M. Gray]] in 1980) is a [[vector quantization]] algorithm to derive a good [[codebook]].

== Description ==

It is similar to the [[k-means]] method in [[data clustering]].

The Linde–Buzo–Gray algorithm may be implemented as follows:

==The '''algorithm''' linde-buzo-gray =='''is'''

'''input''': set of training vectors ''training'', codebook to improve ''old-codebook''

At each iteration, each vector is split into two new vectors.

'''output''': codebook that is twice the size and better or as good as ''old-codebook''

''new-codebook'' ← {}

'''for each''' ''old-codevector'' '''in''' ''old-codebook'' '''do'''

insert ''old-codevector'' into ''new-codebook''

insert ''old-codevector'' + {{epsilon}} into ''new-codebook'' where {{epsilon}} is a small vector

'''return''' lloyd(''new-codebook'', ''training'')

'''algorithm''' lloyd '''is'''

*A initial state: centroid of the training sequence;

'''input''': ''codebook'' to improve, set of training vectors ''training''

*B initial estimation #1: code book of size 2;

'''output''': improved codebook

*C final estimation after [[Lloyd's algorithm|LGA]]: Optimal code book with 2 vectors;

*D initial estimation #2: code book of size 4;

'''do'''

*E final estimation after [[Lloyd's algorithm|LGA]]: Optimal code book with 4 vectors;

''previous-codebook'' ← ''codebook''

''clusters'' ← divide ''training'' into |''codebook''| clusters, where each cluster contains all vectors in ''training'' who are best represented by the corresponding vector in ''codebook''

'''for each''' cluster ''cluster'' in ''clusters'' '''do'''

the corresponding code vector in ''codebook'' ← the centroid of all training vectors in ''cluster''

'''while''' difference in error representing ''training'' between ''codebook'' and ''previous-codebook'' &gt; {{epsilon}}

'''return''' ''codebook''

== References ==

{{reflist}}

* The original paper describing the algorithm, as an extension to [[Lloyd's algorithm]]:

**{{Cite journal | last1 = Linde | first1 = Y. | last2 = Buzo | first2 = A. | last3 = Gray | first3 = R. | author3link = Robert M. Gray| title = An Algorithm for Vector Quantizer Design | doi = 10.1109/TCOM.1980.1094577 | journal = [[IEEE Transactions on Communications]] | volume = 28 | pages = 84–95 | year = 1980 | pmid = | pmc = }}

{{DEFAULTSORT:Linde-Buzo-Gray algorithm}}

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[[Category:Machine learning algorithms]]

[[Category:Artificial neural networks]]

[[Category:Signal processing]]