南京大学学报(自然科学版) ›› 2017, Vol. 53 ›› Issue (6): 1125.
谢 峰1,蔡瑞初1*,陈 薇1,郝志峰1,2
Xie Feng1,Cai Ruichu1*,Chen Wei1,Hao Zhifeng1,2
摘要: 线性非高斯无环模型(LiNGAM)具有在没有任何先验知识的情况下能够仅仅从观察数据中完整地识别因果网络的优势,这使得它得到了越来越多研究者的关注.然而,现有求解LiNGAM模型的算法中一部分存在对初始值敏感,容易陷入局部最优解的问题,一部分存在对于外生变量识别率低的缺陷.为此,提出了一种基于最大最小独立性的因果发现算法.通过引入自适应的独立性判定参数,根据此参数来找出与其余所有变量回归得到的残差都独立的变量,即为外生变量.该算法不仅避免了传统算法对独立性值差异敏感而导致识别率低的问题,而且也避免了不同数据集对固定独立性参数敏感而导致无法识别的缺陷.将该算法应用于虚拟网络和真实网络中,实验结果都表明,各种维度下该算法都优于现有的其他算法.
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