TY - GEN
T1 - Reducing the computational and communication complexity of a distributed optimization for regularized logistic regression
AU - Miya, Nozomi
AU - Masui, Hideyuki
AU - Jinushi, Hajime
AU - Matsushima, Toshiyasu
N1 - Funding Information:
ACKNOWLEDGMENT The authors would like to thank all the members of Matsushima Lab. for their helpful suggestions. Especially, Dr. Shunsuke Horii of Waseda Univ. presented fruitful comments to us. This work was supported by JSPS KAKENHI Grant Numbers JP17K00316, JP17K06446, JP18K11585, and JP19K04914.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85076793139&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85076793139&partnerID=8YFLogxK
U2 - 10.1109/SMC.2019.8914663
DO - 10.1109/SMC.2019.8914663
M3 - Conference contribution
AN - SCOPUS:85076793139
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 3454
EP - 3459
BT - 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
Y2 - 6 October 2019 through 9 October 2019
ER -