Unsupervised Hashing Models
Unsupervised Hashing Models these methods fracture the input feature space using randomly drawn hyperplanes independently of the data distribution. Given the random nature of the learning procedure they are the fastest models at training time but suffer from the disadvantage of requiring long hashcodes and many hashtables to attain a reasonable level of retrieval effectiveness.
Name | Architecture |
A. Andoni, P. Indyk, 2006. Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions |
Shallow |
A. Andoni, P. Indyk, T. Laarhoven, 2015. Practical and Optimal LSH for Angular Distance |
Shallow |
M. Datar, N. Immorlica, P. Indyk, V. Mirrokni, 2004. Locality-sensitive hashing scheme based on p-stable distributions |
Shallow |
A. Gionis, P. Indyk, R. Motwani, 1999. Similarity Search in High Dimensions via Hashing |
Shallow |
S. Moran, R. McCreadie, C. Macdonald, I. Ounis, 2016. Enhancing First Story Detection using Word Embeddings |
Shallow |
S. Petrovic, M. Osborne, V. Lavrenko, 2010. Streaming First Story Detection with application to Twitter |
Shallow |
S. Petrovic, M. Osborne, V. Lavrenko, 2012. Using paraphrases for improving first story detection in news and Twitter |
Shallow |
M. Raginsky, S. Lazebnik, 2009. Locality-sensitive binary codes from shift-invariant kernels |
Shallow |
A. Shrivastava, P. Li, 2014. Densifying One Permutation Hashing via Rotation for Fast Near Neighbor Search |
Shallow |
Fran Silavong, Sean Moran, Antonios Georgiadis, Rohan Saphal, Robert Otter, 2021. DeSkew-LSH based Code-to-Code Recommendation Engine |
Shallow |