Neurocomputing, volume 479, pages 37-46
Decentralized AdaBoost algorithm over sensor networks
Publication type: Journal Article
Publication date: 2022-03-01
Journal:
Neurocomputing
Q1
Q1
SJR: 1.815
CiteScore: 13.1
Impact factor: 5.5
ISSN: 09252312, 18728286
Computer Science Applications
Artificial Intelligence
Cognitive Neuroscience
Abstract
In this paper, we study the decentralized AdaBoost problem over sensor networks, and propose a fully decentralized AdaBoost algorithm, where each sensor can obtain the centralized global solution without transmission of private dataset. By decomposing the centralized cost function into a summation of local ones, we convert decentralized AdaBoost problem into a distributed optimization problem, and design a distributed alternating minimization method to solve it. In order to improve convergence rate, motivated by Nesterov gradient descent method, we propose a fast decentralized AdaBoost algorithm. Then, we prove the convergence of proposed algorithms. Moreover, we deduce decentralized AdaBoost algorithm for logistic regression in detail. The simulations with Spam-Email dataset illustrate the effectiveness of proposed algorithms.
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An X. et al. Decentralized AdaBoost algorithm over sensor networks // Neurocomputing. 2022. Vol. 479. pp. 37-46.
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An X., Hu C., Li Z., Lin H., Liu G. Decentralized AdaBoost algorithm over sensor networks // Neurocomputing. 2022. Vol. 479. pp. 37-46.
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TY - JOUR
DO - 10.1016/j.neucom.2022.01.015
UR - https://doi.org/10.1016/j.neucom.2022.01.015
TI - Decentralized AdaBoost algorithm over sensor networks
T2 - Neurocomputing
AU - An, X
AU - Hu, C
AU - Li, Z
AU - Lin, H
AU - Liu, G
PY - 2022
DA - 2022/03/01
PB - Elsevier
SP - 37-46
VL - 479
SN - 0925-2312
SN - 1872-8286
ER -
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@article{2022_An,
author = {X An and C Hu and Z Li and H Lin and G Liu},
title = {Decentralized AdaBoost algorithm over sensor networks},
journal = {Neurocomputing},
year = {2022},
volume = {479},
publisher = {Elsevier},
month = {mar},
url = {https://doi.org/10.1016/j.neucom.2022.01.015},
pages = {37--46},
doi = {10.1016/j.neucom.2022.01.015}
}
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