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
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