JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2016, Vol. 51 ›› Issue (9): 137-144.doi: 10.6040/j.issn.1671-9352.3.2015.064

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A distributed training method for L1 regularized kernel machines based on filtering mechanism

JI Xin-rong1,2, HOU Cui-qin1, HOU Yi-bin1, ZHAO Bin3   

  1. 1. Beijing Engineering Research Center for IoT Software and Systems, Beijing 100124, China;
    2. School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, Hebei, China;
    3. School of Software Engineering, Beijing University of Technology, Beijing 100124, China
  • Received:2015-08-02 Online:2016-09-20 Published:2016-09-23

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Abstract: To decrease the amount of data transferred and the computing cost during training a kernel machine in wireless sensor network, a distributed training method for L1-regularized Kernel Minimum Square Error machine based on filtering mechanism was proposed. First, filtering mechanism of samples was presented and used on each node. Second, with consistency constraint on the local model of each node and its local optimal one obtained by exchanging the local model with its all neighbours, the distributed optimization problem of L1-regularized Kernel Minimum Square Error machine was solved by Augmented Lagrange Method of Multipliers, and the local optimization problem of L1-regularized Kernel Minimum Square Error machine on each node was solved by Alternating Direction Method of Multipliers. Then, the spares model obtained on each node was transferred to its all neighbor nodes. This process iterates until the local model on each node converges. For carrying out this method,a novel distributed training algorithm for L1-regularized Kernel Minimum Square Error based on filtering of samples was proposed. Simulation results prove the validity of the proposed algorithm in terms of convergence, sparse rate of model, the amount of data transferred and the number of samples used in model training.

Key words: wireless sensor network, L1-regularized, augmented Lagrange method of multipliers, kernel machines, distributed learning, filtering mechanism of samples

CLC Number: 

  • TP181
[1] 吕方旭,张金成,郭相科,等. 基于WSN的战场声目标多特征联合智能分类识别[J]. 科学技术与工程, 2013, 13(35):10713-10721. LÜ Fangxu, ZHANG Jincheng, GUO Xiangke, et al. Multi feature joint intelligent classification and recognition of battlefield acoustic targets based on WSN[J]. Science Technology and Engineering, 2013, 13(35):10713-10721.
[2] TAGHVAEEYAN S, RAJAMANI R. Portable roadside sensors for vehicle counting, classification, and speed measurement[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(1):73-83.
[3] SHAHID N, NAQVI I H, QAISAR BIN S. Quarter-sphere svm: attribute and spatio-temporal correlations based outlier and event detection in wireless sensor networks[J]. IEEE Wireless Communication and Networking Conference: Mobile and Wireless Networks, 2012: 2048-2053.
[4] 刘倩,崔晨,周杭霞. 改进型SVM多类分类算法在无线传感器网络中的应用[J].中国计量学院学报, 2013, 24(3):298-303. LIU Qian, CUI Chen, ZHOU Hangxia. Application of improved SVM multi class classification algorithm in wireless sensor networks[J]. Journal of China Jiliang University, 2013, 24(3):298-303.
[5] 韩屏,李方敏,罗婷. 无线传感器网络的分布式目标跟踪算法[J]. 北京邮电大学学报, 2009, 32(1):90-94. HAN Ping, LI Fangmin, LUO Ting. Distributed target tracking algorithm for wireless sensor networks[J]. Journal of Beijing University of Posts and Telecommunications, 2009, 32(1):90-94.
[6] RAJ A B, RAMESH M V, KULKARNI R V, et al. Security enhancement in wireless sensor networks using machine learning[C] // Proceedings of IEEE 14th International Conference on High Performance Computing and Communications. New York: ACM, 2012: 1264-1269.
[7] SCHÖLKOPF B, SMOLA A. Learning with kernels: support vector machines, regularization, optimization and beyond[J]. MIT Press, 2002: 61-118.
[8] 及歆荣,侯翠琴,侯义斌. 无线传感器网络下线性支持向量机分布式协同训练方法研究[J].电子与信息学报,2015, 37(3):708-714. JI Xinrong, HOU Cuiqin, HOU Yibin. Research on distributed cooperative training method for linear support vector machine in wireless sensor networks[J]. Journal of Electronics and Information, 2015, 37(3):708-714.
[9] GUESTRIN C, BODIK P, THIBAUX R, et al. Distributed regression: an efficient framework for modeling sensor network data[C] // Proceedings of the International Conference on Information Processing in Sensor Networks. New York: ACM, 2004: 1-10.
[10] PREDD J B, KULKARNI S R, POOR H V. Distributed kernel regression: an algorithm for training collaboratively[C] // Proceedings of IEEE Information Theory Workshop. New York: IEEE, 2006: 332-336.
[11] PREDD J B, KULKARNI S R, POOR H V. A Collaborative training algorithm for distributed learning[J]. IEEE Transactions on Information Theory, 2009, 55(4):1856-1870.
[12] FLOURI K, BEFERULL-LOZANO B, TSAKALIDES P. Optimal gossip algorithm for distributed consensus SVM training in wireless sensor networks[C] // Proceedings of the 16th International Conference on Digital Signal Processin(DSP '09). Piscataway: IEEE, 2009: 886-891.
[13] FLOURI K, BEFERULL-LOZANO B, TSAKALIDES P.Training a support-vector machine-based classifier in distributed sensor networks[C] // Proceedings of the 14nd European Signal Processing Conference. Florence:[S.l.] , 2006: 4-8.
[14] YUMAO L, ROYCHOWDHURY V, VANDENBERGHE L. Distributed parallel support vector machines in strongly connected networks[J]. IEEE Transactions on Neural Networks, 2008, 19(7):1167-1178.
[15] XU Jianhua, ZHANG Xuegong, LI Yanda. Kernel MSE algorithm: a unified framework for KFD, LS-SVM and KRR[C] // Proceedings of International Joint Conference on Neural Networks. Piscataway: IEEE, 2001: 1486-1491.
[16] BOYD S, PARIKH N, CHU E. Distributed optimization and statistical learning via the alternating direction method of multipliers[J]. Foundations and Trends in Machine Learning, 2011, 3(1):1-122.
[17] BERTSEKAS D P, TSITSIKLIS J N. Parallel and distributed computation: numerical methods[M]. London: Prentice-Hall, 1997: 243-247.
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