A Survey on Learning to Hash
Microsoft Research Asia (China) · University of Science and Technology of China · +2 more institutions
Abstract
Nearest neighbor search is a problem of finding the data points from the database such that the distances from them to the query point are the smallest. Learning to hash is one of the major solutions to this problem and has been widely studied recently. In this paper, we present a comprehensive survey of the learning to hash algorithms, categorize them according to the manners of preserving the similarities into: pairwise similarity preserving, multiwise similarity preserving, implicit similarity preserving, as well as quantization, and discuss their relations. We separate quantization from pairwise similarity preserving as the objective function is very different though quantization, as we show, can be…
Citation impact
- FWCI
- 42.70
- Percentile
- 100%
- References
- 225
Authors
5Topics & keywords
- Nearest neighbor search
- Pairwise comparison
- Hash function
- Computer science
- Quantization (signal processing)
- Categorization
- Locality-sensitive hashing
- Similarity (geometry)