无线感知网络分布式协同学习稀疏核学习机的理论和算法研究

项目名称： 无线感知网络分布式协同学习稀疏核学习机的理论和算法研究

项目编号： No.61203377

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 自动化学科

项目作者： 侯翠琴

作者单位： 北京工业大学

项目金额： 25万元

中文摘要： 在无线感知网络中,能源和带宽资源都非常宝贵,因此通过相邻节点间的传输共享数据和相互协作在网内分布式协同学习分类器、回归机的方法已成为无线感知网络领域新的研究热点.本课题在分布式优化理论的框架下,基于统计机器学习理论,根据无线感知网络和L1正则化稀疏核方法的特点,研究有效集的预测、数据传输和共享方式、相邻感知节点间的协作对学习算法能量消耗、带宽占用、收敛性、收敛速度等的影响，旨在提出能充分挖掘 L1正则化稀疏核方法的特性及节点间协作关系的低功耗、低通信代价的分布式协同学习算法,并在此基础上基于场理论,研究在学习过程中网内各感知节点的能量损耗情况,提出能动态平衡能量损耗的学习算法.在无线感知网络平台上，用实测数据分析所提算法在能源消耗、带宽占用、收敛速度等方面的表现，并测试学习到的L1正则化稀疏核学习机的稀疏性、预测精度及预测时间.本课题的实施对无线感知网络分布式协同学习的研究具有积极促进作用

中文关键词： 稀疏核方法；分布式学习；无线传感器网络；L1 正则；

英文摘要： Wireless sensor networks are characterized by constrains on energy and bandwidth, which limits the communication overhead. Hence distributed learning in wireless sensor networks to achieve the global optimal classifier or regression estimator by interchanging information and coordination among neighboring nodes, has attracted much attention from scholars and practitioners in different fields recently. In this project, we study the energy-efficient distributed algorithms for learning the L1 regularization sparse kernel methods in wireless sensor networks based on distributed optimization theory and statistical machine learning theory. According to the properties of wireless sensor networks and the L1 regularization loss functions of kernel methods, we first investigate predicting the active set during learning and the mechanism of information transmission and coordination among neighboring nodes in wireless sensor networks to reduce communication overhead and aim to propose the energy-efficient local message-passing algorithms to obtain an optimal or close-to-optimal classifier or regression estimator while minimizing the required amount of information exchange among neighboring sensors. Then on the basis of the above research and filed theory, dynamic energy balancing algorithms are investigated, which choose se

英文关键词： sparse kernel methods；distributed learning；wireless sensor networks；L1 regularized；