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In-Network Linear Regression with Arbitrarily Split Data Matrices

Citation Author(s):
François D. Côté, Ioannis N. Psaromiligkos, Warren J. Gross
Submitted by:
Francois Cote
Last updated:
5 December 2016 - 6:33pm
Document Type:
Poster
Document Year:
2016
Event:
Presenters Name:
François D. Côté
Paper Code:
RMN-P1.13

Abstract 

Abstract: 

We address for the first time the question of how networked agents can collaboratively fit a Morozov-regularized linear model when each agent knows a summand of the regression data. This question generalizes previously studied data-splitting scenarios, which require that the data be partitioned among the agents. To answer the question, we introduce a class of network-structured problems, which contains the regularization problem, and by using the Douglas-Rachford splitting algorithm, we develop a distributed algorithm to solve these problems. We illustrate through simulations that our approach is an effective strategy for fully distributed linear regression.

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