K-Subspaces Quantization for Approximate Nearest Neighbor Search
Author | Ozan E.C. |
Author | Kiranyaz, Mustafa Serkan |
Author | Gabbouj M. |
Available date | 2022-04-26T12:31:23Z |
Publication Date | 2016 |
Publication Name | IEEE Transactions on Knowledge and Data Engineering |
Resource | Scopus |
Identifier | http://dx.doi.org/10.1109/TKDE.2016.2535287 |
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. |
Language | en |
Publisher | IEEE Computer Society |
Subject | Binary 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 |
Type | Article |
Pagination | 1722-1733 |
Issue Number | 7 |
Volume Number | 28 |
Files in this item
Files | Size | Format | View |
---|---|---|---|
There are no files associated with this item. |
This item appears in the following Collection(s)
-
Electrical Engineering [2649 items ]