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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=Transactions on Pattern Analysis and Machine Intelligence|year=2011|volume=33|issue=1|pages=117–128|doi=10.1109/TPAMI.2010.57|url=http://hal.archives-ouvertes.fr/docs/00/51/44/62/PDF/paper_hal.pdf}}</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|url=http://www.sciencedirect.com/science/article/pii/S0031320306002780|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.
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