In-Network Linear Regression with Arbitrarily Split Data Matrices
Optimization and Control
2014-08-06 v1
Abstract
In this paper, we address the problem of how a network of agents can collaboratively fit a linear model when each agent only ever has an arbitrary summand of the regression data. This problem generalizes previously studied data-matrix-splitting scenarios, allowing for some agents to have more measurements of some features than of others and even have measurements that other agents have. We present a variable-centric framework for distributed optimization in a network, and use this framework to develop a proximal algorithm, based on the Douglas-Rachford method, that solves the problem.
Cite
@article{arxiv.1408.1073,
title = {In-Network Linear Regression with Arbitrarily Split Data Matrices},
author = {François D. Côté and Ioannis N. Psaromiligkos and Warren J. Gross},
journal= {arXiv preprint arXiv:1408.1073},
year = {2014}
}
Comments
3 pages, 3 figures