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المؤلفOzan E.C.
المؤلفKiranyaz, Mustafa Serkan
المؤلفGabbouj M.
تاريخ الإتاحة2022-04-26T12:31:23Z
تاريخ النشر2016
اسم المنشورIEEE Transactions on Knowledge and Data Engineering
المصدرScopus
المعرّفhttp://dx.doi.org/10.1109/TKDE.2016.2535287
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84976384988&doi=10.1109%2fTKDE.2016.2535287&partnerID=40&md5=4ae8cd911fb19d53dc4d30795e5bc9b9
معرّف المصادر الموحدhttp://hdl.handle.net/10576/30632
الملخصApproximate 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.
اللغةen
الناشرIEEE Computer Society
الموضوعBinary 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
العنوانK-Subspaces Quantization for Approximate Nearest Neighbor Search
النوعArticle
الصفحات1722-1733
رقم العدد7
رقم المجلد28
dc.accessType Abstract Only


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