Fetching the paper…
Reading the bibliography…
Recently, the method of b-bit minwise hashing has been applied to large-scale linear learning and sublinear time near-neighbor search.
An algorithm for finding nearest neighbors
Jerome H. Friedman, F. Baskett, and L. Shustek · 1975
Earlier work this paper cites.
Universal classes of hash functions (extended abstract)
J. Lawrence Carter and Mark N. Wegman · 1977
Earlier work this paper cites.
Syntactic clustering of the web
Andrei Z. Broder, Steven C. Glassman, Mark S. Manasse, and Geoffrey Zweig · 1997
Earlier work this paper cites.
Min-wise independent permutations (extended abstract)
Andrei Z. Broder, Moses Charikar, Alan M. Frieze, and Michael Mitzenmacher · 1998
Earlier work this paper cites.
Approximate nearest neighbors: Towards removing the curse of dimensionality
Piotr Indyk and Rajeev Motwani · 1998
Earlier work this paper cites.
Table of Integrals, Series, and Products
Izrail S. Gradshteyn and Iosif M. Ryzhik · 2000
Earlier work this paper cites.
A large-scale study of the evolution of web pages
Dennis Fetterly, Mark Manasse, Marc Najork, and Janet L. Wiener · 2003
Earlier work this paper cites.
Video google: a text retrieval approach to object matching in videos
Josef Sivic and Andrew Zisserman · 2003
Earlier work this paper cites.
An improved data stream summary: the count-min sketch and its applications
Graham Cormode and S. Muthukrishnan · 2005
Cited alongside, same era.
Training linear svms in linear time
Thorsten Joachims · 2006
Cited alongside, same era.
Very sparse random projections
Ping Li, Trevor J. Hastie, and Kenneth W. Church · 2006
Cited alongside, same era.
Using sketches to estimate associations
Ping Li and Kenneth W. Church · 2007
Cited alongside, same era.
Pegasos: Primal estimated sub-gradient solver for svm
Shai Shalev-Shwartz, Yoram Singer, and Nathan Srebro · 2007
Cited alongside, same era.
Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
Alexandr Andoni and Piotr Indyk · 2008
Cited alongside, same era.
One sketch for all: Theory and applications of conditional random sampling
Ping Li, Kenneth W. Church, and Trevor J. Hastie · 2008
Later among the works it cites.
Lessons learned developing a practical large scale machine learning system
Simon Tong · 2008
Later among the works it cites.
Hash kernels for structured data
Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alex Smola, and S.V.N. Vishwanathan · 2009
Later among the works it cites.
Feature hashing for large scale multitask learning
Kilian Weinberger, Anirban Dasgupta, John Langford, Alex Smola, and Josh Attenberg · 2009
Later among the works it cites.
Theory and applications b-bit minwise hashing
Ping Li and Arnd Christian König · 2011
Later among the works it cites.
Hashing algorithms for large-scale learning
Ping Li, Anshumali Shrivastava, Joshua Moore, and Arnd Christian König · 2011
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Liblinear: A library for large linear classification
Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, and Chih-Jen Lin · 2008
Cited alongside, same era.
A dual coordinate descent method for large-scale linear svm
Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin, S. Sathiya Keerthi, and S. Sundararajan · 2008
Cited alongside, same era.
http://leon.bottou.org/projects/sgd
Leon Bottou
Cited in the paper.
Later among the works it cites.
Fast near neighbor search in high-dimensional binary data
Anshumali Shrivastava and Ping Li · 2012
Closest in time.