Feature Hashing for Large Scale Multitask Learning
Artificial Intelligence
2010-02-27 v5
Abstract
Empirical evidence suggests that hashing is an effective strategy for dimensionality reduction and practical nonparametric estimation. In this paper we provide exponential tail bounds for feature hashing and show that the interaction between random subspaces is negligible with high probability. We demonstrate the feasibility of this approach with experimental results for a new use case -- multitask learning with hundreds of thousands of tasks.
Cite
@article{arxiv.0902.2206,
title = {Feature Hashing for Large Scale Multitask Learning},
author = {Kilian Weinberger and Anirban Dasgupta and Josh Attenberg and John Langford and Alex Smola},
journal= {arXiv preprint arXiv:0902.2206},
year = {2010}
}
Comments
Fixed broken theorem