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MinHash and SimHash are the two widely adopted Locality Sensitive Hashing (LSH) algorithms for large-scale data processing applications.
An algorithm for finding nearest neighbors
Jerome H. Friedman, F. Baskett, and L. Shustek · 1975
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Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming
Michel X. Goemans and David P. Williamson · 1995
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On the resemblance and containment of documents
Andrei Z. Broder · 1997
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Min-wise independent permutations
Andrei Z. Broder, Moses Charikar, Alan M. Frieze, and Michael Mitzenmacher · 1998
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Approximate nearest neighbors: Towards removing the curse of dimensionality
Piotr Indyk and Rajeev Motwani · 1998
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Support vector machines for histogram-based image classification
Olivier Chapelle, Patrick Haffner, and Vladimir N. Vapnik · 1999
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Similarity estimation techniques from rounding algorithms
Moses S. Charikar · 2002
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A large-scale study of the evolution of web pages
Dennis Fetterly, Mark Manasse, Marc Najork, and Janet L. Wiener · 2003
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E2lsh: Exact euclidean locality sensitive hashing
Alexandr Andoni and Piotr Indyk · 2004
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Hilbertian metrics and positive definite kernels on probability measures
Matthias Hein and Olivier Bousquet · 2005
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Finding near-duplicate web pages: a large-scale evaluation of algorithms
Monika Rauch Henzinger · 2006
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Conditional random sampling: A sketch-based sampling technique for sparse data
Ping Li, Kenneth W. Church, and Trevor J. Hastie · 2006
Cited alongside, same era.
Towards optimal bag-of-features for object categorization and semantic video retrieval
Yugang Jiang, Chongwah Ngo, and Jun Yang · 2007
Cited alongside, same era.
A scalable pattern mining approach to web graph compression with communities
Gregory Buehrer and Kumar Chellapilla · 2008
Cited alongside, same era.
Opinion spam and analysis
Nitin Jindal and Bing Liu · 2008
Cited alongside, same era.
Lessons learned developing a practical large scale machine learning system
Simon Tong · 2008
Cited alongside, same era.
Less is more: sampling the neighborhood graph makes salsa better and faster
Marc Najork, Sreenivas Gollapudi, and Rina Panigrahy · 2009
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Nearest-neighbor caching for content-match applications
Sandeep Pandey, Andrei Broder, Flavio Chierichetti, Vanja Josifovski, Ravi Kumar, and Sergei Vassilvitskii · 2009
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Sibyl: a system for large scale machine learning
Tushar Chandra, Eugene Ie, Kenneth Goldman, Tomas Lloret Llinares, Jim McFadden, Fernando Pereira, Joshua Redstone, Tal Shaked, and Yoram Singer · 2010
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b-bit minwise hashing
Ping Li and Arnd Christian König · 2010
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A reliable effective terascale linear learning system
Alekh Agarwal, Olivier Chapelle, Miroslav Dudik, and John Langford · 2011
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Hashing algorithms for large-scale learning
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Tracking web spam with html style similarities
Tanguy Urvoy, Emmanuel Chauveau, Pascal Filoche, and Thomas Lavergne · 2008
Cited alongside, same era.
Spectral hashing
Yair Weiss, Antonio Torralba, and Robert Fergus · 2008
Cited alongside, same era.
On compressing social networks
Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, Michael Mitzenmacher, Alessandro Panconesi, and Prabhakar Raghavan · 2009
Cited alongside, same era.
An axiomatic approach for result diversification
Sreenivas Gollapudi and Aneesh Sharma · 2009
Cited alongside, same era.
Ping Li, Anshumali Shrivastava, Joshua Moore, and Arnd Christian König · 2011
Later among the works it cites.
One permutation hashing
Ping Li, Art B Owen, and Cun-Hui Zhang · 2012
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Fast near neighbor search in high-dimensional binary data
Anshumali Shrivastava and Ping Li · 2012
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b-bit minwise hashing in practice
Ping Li, Anshumali Shrivastava, and Arnd Christian König · 2013
Later among the works it cites.
Densifying one permutation hashing via rotation for fast near neighbor search
Anshumali Shrivastava and Ping Li · 2014
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