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Competitive Quantization for Approximate Nearest Neighbor Search

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Details

Original languageEnglish
Pages (from-to)2884-2894
JournalIEEE Transactions on Knowledge and Data Engineering
Volume28
Issue number11
DOIs
Publication statusPublished - 2016
Publication typeA1 Journal article-refereed

Abstract

In this study, we propose a novel vector quantization algorithm for Approximate Nearest Neighbor (ANN) search, based on a joint competitive learning strategy and hence called as competitive quantization (CompQ). CompQ is a hierarchical algorithm, which iteratively minimizes the quantization error by jointly optimizing the codebooks in each layer, using a gradient decent approach. An extensive set of experimental results and comparative evaluations show that CompQ outperforms the-state-of-the-art while retaining a comparable computational complexity.

Publication forum classification

Field of science, Statistics Finland