Name
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. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP) | Distributed algorithm, regression, splitting |
Field | DocType | ISSN |
Mathematical optimization,Regression,Linear model,Matrix (mathematics),Theoretical computer science,Convex function,Distributed algorithm,Regularization (mathematics),Distributed database,Mathematics,Linear regression | Conference | 2376-4066 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| François D. Côté | 1 | 0 | 1.35 |
| Ioannis N. Psaromiligkos | 2 | 23 | 8.08 |
| Warren J. Gross | 3 | 1106 | 113.38 |