Distributed Linear Model Clustering over Networks: A Tree-Based Fused-Lasso ADMM Approach
Machine Learning
2019-05-29 v1 Distributed, Parallel, and Cluster Computing
Machine Learning
Networking and Internet Architecture
Statistics Theory
Statistics Theory
Abstract
In this work, we consider to improve the model estimation efficiency by aggregating the neighbors' information as well as identify the subgroup membership for each node in the network. A tree-based penalty is proposed to save the computation and communication cost. We design a decentralized generalized alternating direction method of multiplier algorithm for solving the objective function in parallel. The theoretical properties are derived to guarantee both the model consistency and the algorithm convergence. Thorough numerical experiments are also conducted to back up our theory, which also show that our approach outperforms in the aspects of the estimation accuracy, computation speed and communication cost.
Cite
@article{arxiv.1905.11549,
title = {Distributed Linear Model Clustering over Networks: A Tree-Based Fused-Lasso ADMM Approach},
author = {Xin Zhang and Jia Liu and Zhengyuan Zhu},
journal= {arXiv preprint arXiv:1905.11549},
year = {2019}
}