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=== Use in pattern recognition ===
VQ was also used in the eighties for speech<ref>{{cite journal|last=Burton|first=D. K.|author2=Shore, J. E. |author3=Buck, J. T. |title=A generalization of isolated word recognition using vector quantization|journal=IEEE International Conference on Acoustics Speech and Signal Processing ICASSP|volume=8|year=1983|pages=1021–1024|doi=10.1109/ICASSP.1983.1171915}}</ref> and speaker recognition.<ref>{{cite journal|last=Soong|first=F.|author2=A. Rosenberg |author3=L. Rabiner |author4=B. Juang |title=A vector Quantization approach to Speaker Recognition|journal=IEEE Proceedings International Conference on Acoustics, Speech and Signal Processing ICASSP|year=1985|volume=1|pages=387–390|doi=10.1109/ICASSP.1985.1168412|s2cid=8970593|url=https://www.semanticscholar.org/paper/9e1d50d98ae09c15354dbcb126609e337d3dc6fb}}</ref>
Recently it has also been used for efficient nearest neighbor search
<ref>{{cite journal|author=H. Jegou |author2=M. Douze |author3=C. Schmid|title=Product Quantization for Nearest Neighbor Search|journal=IEEE Transactions on Pattern Analysis and Machine Intelligence|year=2011|volume=33|issue=1|pages=117–128|doi=10.1109/TPAMI.2010.57|pmid=21088323 |url=http://hal.archives-ouvertes.fr/docs/00/51/44/62/PDF/paper_hal.pdf|citeseerx=10.1.1.470.8573 |s2cid=5850884 }}</ref>
and on-line signature recognition.<ref>{{cite journal|last=Faundez-Zanuy|first=Marcos|title=offline and On-line signature recognition based on VQ-DTW|journal=Pattern Recognition|year=2007|volume=40|issue=3|pages=981–992|doi=10.1016/j.patcog.2006.06.007}}</ref>
In [[pattern recognition]] applications, one codebook is constructed for each class (each class being a user in biometric applications) using acoustic vectors of this user. In the testing phase the quantization distortion of a testing signal is worked out with the whole set of codebooks obtained in the training phase. The codebook that provides the smallest vector quantization distortion indicates the identified user.
The main advantage of VQ in [[pattern recognition]] is its low computational burden when compared with other techniques such as [[dynamic time warping]] (DTW) and [[hidden Markov model]] (HMM). The main drawback when compared to DTW and HMM is that it does not take into account the temporal evolution of the signals (speech, signature, etc.) because all the vectors are mixed up. In order to overcome this problem a multi-section codebook approach has been proposed.<ref>{{cite journal|last=Faundez-Zanuy|first=Marcos|author2=Juan Manuel Pascual-Gaspar |title=Efficient On-line signature recognition based on Multi-section VQ|journal=Pattern Analysis and Applications|year=2011|volume=14|issue=1|pages=37–45|doi=10.1007/s10044-010-0176-8|s2cid=24868914|url=https://www.semanticscholar.org/paper/acf19e33b76ca5520e85e5c1be54c9920aa590b1}}</ref> The multi-section approach consists of modelling the signal with several sections (for instance, one codebook for the initial part, another one for the center and a last codebook for the ending part).
=== Use as clustering algorithm ===
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