K-Subspaces Quantization Subspaces for Approximate Nearest Neighbor Search
Research output: Contribution to journal › Article › Scientific › peer-review
|Journal||IEEE Transactions on Knowledge and Data Engineering|
|Publication status||Published - 2016|
|Publication type||A1 Journal article-refereed|
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.
- Approximate Nearest Neighbor Search, Vector Quantization, Large-scale learning, Big data