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两两关系马尔科夫网的自适应组稀疏化学习

刘建伟 任正平 刘泽宇 黎海恩 罗雄麟

刘建伟, 任正平, 刘泽宇, 黎海恩, 罗雄麟. 两两关系马尔科夫网的自适应组稀疏化学习. 自动化学报, 2015, 41(8): 1419-1437. doi: 10.16383/j.aas.2015.c140682
引用本文: 刘建伟, 任正平, 刘泽宇, 黎海恩, 罗雄麟. 两两关系马尔科夫网的自适应组稀疏化学习. 自动化学报, 2015, 41(8): 1419-1437. doi: 10.16383/j.aas.2015.c140682
LIU Jian-Wei, REN Zheng-Ping, LIU Ze-Yu, LI Hai-En, LUO Xiong-Lin. Adaptive Group Sparse Learning of Pairwise Markov Network. ACTA AUTOMATICA SINICA, 2015, 41(8): 1419-1437. doi: 10.16383/j.aas.2015.c140682
Citation: LIU Jian-Wei, REN Zheng-Ping, LIU Ze-Yu, LI Hai-En, LUO Xiong-Lin. Adaptive Group Sparse Learning of Pairwise Markov Network. ACTA AUTOMATICA SINICA, 2015, 41(8): 1419-1437. doi: 10.16383/j.aas.2015.c140682

两两关系马尔科夫网的自适应组稀疏化学习

doi: 10.16383/j.aas.2015.c140682
基金项目: 

中国石油大学(北京)基础学科研究基金项目(JCXK-2011-07)资助

详细信息
    作者简介:

    任正平 中国石油大学(北京)地球物理与信息工程学院硕士研究生.主要研究方向为机器学习.E-mail:renzhengping1225@sina.com

Adaptive Group Sparse Learning of Pairwise Markov Network

Funds: 

Supported by Foundation Sciences China University of Petroleum (JCXK-2011-07)

  • 摘要: 稀疏化学习能显著降低无向图模型的参数学习与结构学习的复杂性, 有效地处理无向图模型的学习问题. 两两关系马尔科夫网在多值变量情况下, 每条边具有多个参数, 本文对此给出边参数向量的组稀疏化学习, 提出自适应组稀疏化, 根据参数向量的模大小自适应调整惩罚程度. 本文不仅对比了不同边势情况下的稀疏化学习性能, 为了加速模型在复杂网络中的训练过程, 还对目标函数进行伪似然近似、平均场自由能近似和Bethe自由能近似. 本文还给出自适应组稀疏化目标函数分别使用谱投影梯度算法和投 影拟牛顿算法时的最优解, 并对比了两种优化算法进行稀疏化学习的性能. 实验表明自适 应组稀疏化具有良好的性能.
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出版历程
  • 收稿日期:  2014-09-24
  • 修回日期:  2015-02-16
  • 刊出日期:  2015-08-20

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