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b-Bit Minwise Hashing for Large-Scale Learning

Ping Li, Anshumali Shrivastava, Joshua Moore, and Arnd Christian König


Minwise hashing is a standard technique in the context of search for efficiently computing set similarities. The recent development of b-bit minwise hashing provides a substantial improvement by storing only the lowest b bits of each hashed value. In this paper, we demonstrate that b-bit minwise hashing can be naturally integrated with linear learning algorithms such as linear SVM and logistic regression, to solve large-scale and high-dimensional statistical learning tasks, especially when the data do not fit in memory. We compare b-bit minwise hashing with the Count-Min (CM) and Vowpal Wabbit (VW) algorithms, which have essentially the same variances as random projections. Our theoretical and empirical comparisons illustrate that b-bit minwise hashing is significantly more accurate (at the same storage cost) than VW (and random projections) for binary data.


Publication typeInproceedings
Published inBig Learning 2011: NIPS 2011 Workshop on Algorithms, Systems, and Tools for Learning at Scale
PublisherNeural Information Processing Foundation
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