Unsupervised Hashing Models | Awesome Learning to Hash Add your paper to Learning2Hash

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.
NameArchitecture
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