Distributed Ridge Regression with Feature Partitioning

被引：0

作者：

Gratton, Cristiano ^{[1
]}

Venkategowda, Naveen K. D. ^{[1
]}

Arablouei, Reza ^{[2
]}

Werner, Stefan ^{[1
]}

机构：

[1] Norwegian Univ Sci & Technol, Dept Elect Syst, Trondheim, Norway

[2] CSIROs Data61, Pullenvale, Qld 4069, Australia

来源：

2018 CONFERENCE RECORD OF 52ND ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS, AND COMPUTERS | 2018年

关键词：

RECURSIVE LEAST-SQUARES; SENSOR NETWORKS; ADMM;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We develop a new distributed algorithm to solve the ridge regression problem with feature partitioning of the observation matrix. The proposed algorithm, named D-Ridge, is based on the alternating direction method of multipliers (ADMM) and estimates the parameters when the observation matrix is distributed among different agents with feature (or vertical) partitioning. We formulate the associated ridge regression problem as a distributed convex optimization problem and utilize the ADMM to obtain an iterative solution. Numerical results demonstrate that. D-Ridge converges faster than its diffusion-based contender does.

引用

页码：1423 / 1427

页数：5

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