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AuthorOzan E.C.
AuthorKiranyaz, Mustafa Serkan
AuthorGabbouj M.
Available date2022-04-26T12:31:23Z
Publication Date2016
Publication NameIEEE Transactions on Knowledge and Data Engineering
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/TKDE.2016.2535287
URIhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84976384988&doi=10.1109%2fTKDE.2016.2535287&partnerID=40&md5=4ae8cd911fb19d53dc4d30795e5bc9b9
URIhttp://hdl.handle.net/10576/30632
AbstractApproximate 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.
Languageen
PublisherIEEE Computer Society
SubjectBinary codes
Clustering algorithms
Iterative methods
Nearest neighbor search
Vectors
Approximate nearest neighbors (ANN)
Computational costs
Large-scale datasets
large-scale retrieval
Quantization errors
Quantization schemes
State-of-the-art algorithms
Sub-Space Clustering
Vector quantization
TitleK-Subspaces Quantization for Approximate Nearest Neighbor Search
TypeArticle
Pagination1722-1733
Issue Number7
Volume Number28
dc.accessType Abstract Only


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