K-Subspaces Quantization for Approximate Nearest Neighbor Search
الملخص
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.
معرّف المصادر الموحد
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84976384988&doi=10.1109%2fTKDE.2016.2535287&partnerID=40&md5=4ae8cd911fb19d53dc4d30795e5bc9b9المجموعات
- الهندسة الكهربائية [2616 items ]