TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

K-Subspaces Quantization Subspaces for Approximate Nearest Neighbor Search

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Sivut1722-1733
JulkaisuIEEE Transactions on Knowledge and Data Engineering
Vuosikerta28
Numero7
DOI - pysyväislinkit
TilaJulkaistu - 2016
OKM-julkaisutyyppiA1 Alkuperäisartikkeli

Tiivistelmä

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.

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