Reducing the computational and communication complexity of a distributed optimization for regularized logistic regression

Nozomi Miya, Hideyuki Masui, Hajime Jinushi, Toshiyasu Matsushima

研究成果: Conference contribution

抄録

In this paper, we propose a new distributed optimization method that computes a Lasso estimator for logistic regression in the case when two parties have explanatory variables corresponding to distinct attributes. An existing protocol using the alternating direction method of multipliers (ADMM) for linear regression can be applied to logistic regression. However, this protocol needs an underlying iterative method such as the gradient method. We show that the proposed protocol using the generalized Bregman ADMM, which removes the necessity to use the underlying iterative method, requires lower computational and communication complexity.

本文言語English
ホスト出版物のタイトル2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ページ3454-3459
ページ数6
ISBN(電子版)9781728145693
DOI
出版ステータスPublished - 2019 10
外部発表はい
イベント2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019 - Bari, Italy
継続期間: 2019 10 62019 10 9

出版物シリーズ

名前Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
2019-October
ISSN(印刷版)1062-922X

Conference

Conference2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
国/地域Italy
CityBari
Period19/10/619/10/9

ASJC Scopus subject areas

  • 電子工学および電気工学
  • 制御およびシステム工学
  • 人間とコンピュータの相互作用

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