RaBitQ: Quantizing High-Dimensional Vectors with a Theoretical Error Bound for Approximate Nearest Neighbor Search
Abstract
References
Index Terms
- RaBitQ: Quantizing High-Dimensional Vectors with a Theoretical Error Bound for Approximate Nearest Neighbor Search
Recommendations
Order preserving hashing for approximate nearest neighbor search
MM '13: Proceedings of the 21st ACM international conference on MultimediaIn this paper, we propose a novel method to learn similarity-preserving hash functions for approximate nearest neighbor (NN) search. The key idea is to learn hash functions by maximizing the alignment between the similarity orders computed from the ...
Optimized residual vector quantization for efficient approximate nearest neighbor search
In this paper, an optimized residual vector quantization-based approach is presented for improving the quality of vector quantization and approximate nearest neighbor search. The main contributions are as follows. Based on residual vector quantization (...
Codebook-softened product quantization for high accuracy approximate nearest neighbor search
AbstractProduct quantization (PQ) is a fundamental technique for approximate nearest neighbor (ANN) search in many applications such as information retrieval, computer vision and pattern recognition. In the existing PQ-based methods for ...
Comments
Information & Contributors
Information
Published In
![cover image Proceedings of the ACM on Management of Data](/cms/asset/ecdeed66-6905-4ab2-a277-fae85803a5a9/3670010.cover.jpg)
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Author Tags
Qualifiers
- Research-article
Funding Sources
- Ministry of Education, Singapore
- Ministry of Education, Singapore
- Ministry of Education, Singapore
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 398Total Downloads
- Downloads (Last 12 months)398
- Downloads (Last 6 weeks)228
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in