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K-Subspaces Quantization Subspaces for Approximate Nearest Neighbor Search

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Details

Original languageEnglish
Pages (from-to)1722-1733
JournalIEEE Transactions on Knowledge and Data Engineering
Volume28
Issue number7
DOIs
Publication statusPublished - 2016
Publication typeA1 Journal article-refereed

Abstract

Approximate Nearest Neighbor (ANN) search has become a popular approach for performing fast and efficient retrieval on very large-scale datasets in recent years, as the size and dimension of data grow continuously. In this paper, we propose a novel vector quantization method for ANN search which enables faster and more accurate retrieval on publicly available datasets. We define vector quantization as a multiple affine subspace learning problem and explore the quantization centroids on multiple affine subspaces. We propose an iterative approach to minimize the quantization error in order to create a novel quantization scheme, which outperforms the state-of-the-art algorithms. The computational cost of our method is also comparable to that of the competing methods.

Keywords

  • Approximate Nearest Neighbor Search, Vector Quantization, Large-scale learning, Big data

Publication forum classification

Field of science, Statistics Finland