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Sensors 2016, 16(7), 1021; doi:10.3390/s16071021

A Distributed Learning Method for ℓ 1 -Regularized Kernel Machine over Wireless Sensor Networks

1
Beijing Engineering Research Center for IOT Software and Systems, Beijing 100124, China
2
School of Information & Electrical Engineering, Hebei University of Engineering, Handan 056038, China
*
Author to whom correspondence should be addressed.
Academic Editors: Xue-bo Jin, Feng-bao Yang, Shuli Sun and Hong Wei
Received: 14 March 2016 / Revised: 23 June 2016 / Accepted: 24 June 2016 / Published: 1 July 2016
(This article belongs to the Special Issue Advances in Multi-Sensor Information Fusion: Theory and Applications)
View Full-Text   |   Download PDF [880 KB, uploaded 1 July 2016]   |  

Abstract

In wireless sensor networks, centralized learning methods have very high communication costs and energy consumption. These are caused by the need to transmit scattered training examples from various sensor nodes to the central fusion center where a classifier or a regression machine is trained. To reduce the communication cost, a distributed learning method for a kernel machine that incorporates 1 norm regularization ( 1 -regularized) is investigated, and a novel distributed learning algorithm for the 1 -regularized kernel minimum mean squared error (KMSE) machine is proposed. The proposed algorithm relies on in-network processing and a collaboration that transmits the sparse model only between single-hop neighboring nodes. This paper evaluates the proposed algorithm with respect to the prediction accuracy, the sparse rate of model, the communication cost and the number of iterations on synthetic and real datasets. The simulation results show that the proposed algorithm can obtain approximately the same prediction accuracy as that obtained by the batch learning method. Moreover, it is significantly superior in terms of the sparse rate of model and communication cost, and it can converge with fewer iterations. Finally, an experiment conducted on a wireless sensor network (WSN) test platform further shows the advantages of the proposed algorithm with respect to communication cost. View Full-Text
Keywords: kernel machines; distributed learning; 1 norm regularization (1-regularized); kernel minimum mean squared error (KMSE); wireless sensor network (WSN) kernel machines; distributed learning; 1 norm regularization (1-regularized); kernel minimum mean squared error (KMSE); wireless sensor network (WSN)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Ji, X.; Hou, C.; Hou, Y.; Gao, F.; Wang, S. A Distributed Learning Method for ℓ 1 -Regularized Kernel Machine over Wireless Sensor Networks. Sensors 2016, 16, 1021.

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