Decentralized AdaBoost algorithm over sensor networks
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 o...
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| Vydané v: | Neurocomputing (Amsterdam) Ročník 479; s. 37 - 46 |
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| Jazyk: | English |
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Elsevier B.V
28.03.2022
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| ISSN: | 0925-2312, 1872-8286 |
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| 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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| AbstractList | 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. |
| Author | Liu, Gang Hu, Chen Li, Zhenhua Lin, Haoshen An, Xibin |
| Author_xml | – sequence: 1 givenname: Xibin surname: An fullname: An, Xibin organization: Rocket Force University of Engineering, Xi’an 710025, China – sequence: 2 givenname: Chen surname: Hu fullname: Hu, Chen email: chenh628@hotmail.com organization: Rocket Force University of Engineering, Xi’an 710025, China – sequence: 3 givenname: Zhenhua surname: Li fullname: Li, Zhenhua organization: Science and Technology on Complex Aircraft Systems Simulation Laboratory, Beijing 100094, China – sequence: 4 givenname: Haoshen surname: Lin fullname: Lin, Haoshen organization: Rocket Force University of Engineering, Xi’an 710025, China – sequence: 5 givenname: Gang surname: Liu fullname: Liu, Gang organization: Rocket Force University of Engineering, Xi’an 710025, China |
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| SubjectTerms | Classification Decentralized AdaBoost Distributed optimization Sensor networks |
| Title | Decentralized AdaBoost algorithm over sensor networks |
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