DUAL-LOCO: Distributing Statistical Estimation Using Random Projections
Machine Learning
2016-08-04 v2 Distributed, Parallel, and Cluster Computing
Machine Learning
Abstract
We present DUAL-LOCO, a communication-efficient algorithm for distributed statistical estimation. DUAL-LOCO assumes that the data is distributed according to the features rather than the samples. It requires only a single round of communication where low-dimensional random projections are used to approximate the dependences between features available to different workers. We show that DUAL-LOCO has bounded approximation error which only depends weakly on the number of workers. We compare DUAL-LOCO against a state-of-the-art distributed optimization method on a variety of real world datasets and show that it obtains better speedups while retaining good accuracy.
Cite
@article{arxiv.1506.02554,
title = {DUAL-LOCO: Distributing Statistical Estimation Using Random Projections},
author = {Christina Heinze and Brian McWilliams and Nicolai Meinshausen},
journal= {arXiv preprint arXiv:1506.02554},
year = {2016}
}
Comments
13 pages