Open AccessOptimised quantisation method for approximate nearest neighbour search
Author(s) -
Wang Xing,
Chen Ji,
Yu Jiangxu
Publication year - 2017
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2016.2810
Subject(s) - nearest neighbour , pattern recognition (psychology) , construct (python library) , algorithm , k nearest neighbors algorithm , group (periodic table) , mathematics , artificial intelligence , computer science , physics , quantum mechanics , programming language
We propose optimised group quantisation (OGQ) for approximate nearest neighbour (ANN) search. Specifically, we construct a group of codebooks and select a group of codewords from the codebooks to approximate the original data such that small quantisation error is obtained. We also propose an effective learning algorithm for optimisation. The experiments show that OGQ can significantly outperform several state‐of‐the‐art ANN methods.