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GGMC算法目标函数值实验分析与算法改进

Experimental analysis on object function value of GGMC algorithm and algorithm improvement
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摘要 针对贪心最大割图半监督学习算法(简称GGMC)计算复杂度较高的问题,提出一种改进的贪心最大割图半监督学习算法(简称GGMC-Estop)。依据对GGMC算法优化过程中目标函数变化趋势的实验分析,采取两种在迭代初期停止GGMC算法运行策略,继而通过一次标准的标签传播步骤预测图上所有样本的标记来实施对GGMC的改进。典型数据集的仿真实验结果表明,在取得相近分类性能的同时,改进算法在计算速度上有很大的提高。 Aiming at the problem of high computational complexity of Greed Max-Cut Graph semi-supervised learning algorithm（GGMC）, an improved Greed Max-Cut Graph semi-supervised learning algorithm based on Early stopping strategy, called GGMC-Estop, is proposed. According to the experimental analysis on that object function value in optimi-zation procedure of GGMC, the algorithm is improved in which two early stopping strategies are applied to stop GGMC training and prediction. Standard propagation is used to predict the label of data over the whole graph in one step. Experi-mental results on typical data sets show that the computational amount using the improved algorithm is far less than that of using GGMC algorithm, while the performance of classification for these two algorithms is almost approximate.
作者 修宇 王骏 王忠群 皇苏斌
机构地区 安徽工程大学计算机与信息学院 IBM T J Watson研究中心
出处 《计算机工程与应用》 CSCD 北大核心 2015年第12期111-117,188,共8页 Computer Engineering and Applications
基金 国家自然科学基金(No.71371012,No.71171002) 教育部人文社科规划项目(No.13YJA630098) 安徽省优秀青年人才基金重点项目(No.2013SQRL034Z) 安徽省高校省级科学研究项目(No.TSKJ2014B10) 安徽工程大学青年基金项目(No.2013YQ30)
关键词 图半监督学习 贪心最大割 早期停止策略 目标函数值 graph semi-supervised learning greedy max-cut early stopping strategy object function value
分类号 TP181 [自动化与计算机技术—控制理论与控制工程]
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参考文献15

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二级参考文献14

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计算机工程与应用
2015年 第12期

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