Hashing Algorithms for Large-Scale Learning
Abstract
In this paper, we first demonstrate that b-bit minwise hashing, whose estimators are positive definite kernels, can be naturally integrated with learning algorithms such as SVM and logistic regression. We adopt a simple scheme to transform the nonlinear (resemblance) kernel into linear (inner product) kernel; and hence large-scale problems can be solved extremely efficiently. Our method provides a simple effective solution to large-scale learning in massive and extremely high-dimensional datasets, especially when data do not fit in memory. We then compare b-bit minwise hashing with the Vowpal Wabbit (VW) algorithm (which is related the Count-Min (CM) sketch). Interestingly, VW has the same variances as random projections. Our theoretical and empirical comparisons illustrate that usually -bit minwise hashing is significantly more accurate (at the same storage) than VW (and random projections) in binary data. Furthermore, -bit minwise hashing can be combined with VW to achieve further improvements in terms of training speed, especially when is large.
Cite
@article{arxiv.1106.0967,
title = {Hashing Algorithms for Large-Scale Learning},
author = {Ping Li and Anshumali Shrivastava and Joshua Moore and Arnd Christian Konig},
journal= {arXiv preprint arXiv:1106.0967},
year = {2011}
}