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Poster
Hashing Algorithms for Large-Scale Learning
Ping Li · Anshumali Shrivastava · Joshua L Moore · Arnd C König

Tue Dec 13 08:45 AM -- 02:59 PM (PST) @
in Poster Session »
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.
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Author Information

Ping Li (Baidu Research USA)
Anshumali Shrivastava (Rice University / ThirdAI Corp.)
Joshua L Moore (Cornell University)
Arnd C König (Microsoft Research)

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