Feature Hashing for Large Scale Multitask Learning

Details Feature Hashing for Large Scale Multitask Learning Feature Hashing for Large Scale Multitask Learning

2009-02-12

arxiv.org/abs/0902.2206v5

Computer Science

Artificial Intelligence

Fixed broken theorem

Scientific paper

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.

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