| 英文标题 | Causal discovery algorithm based on multiset canonical correlation variables |
| 作者英文名 | Chen Wei, Cai Ruichu, Wu Yunjin, Xie Feng, Hao Zhifeng |
| 机构英文名 | 1.School of Computer Science,Guangdong University of Technology,Guangzhou 510006,China;2.School of Mathematics & Big Data,Foshan University,Foshan Guangdong 528225,China |
| 英文摘要 | Existing causal discovery algorithms are mainly based on the observed variables, and cannot be applied to the causal discovery among multiple sets of observed variables. Hence, this paper proposed a multiset canonical correlation variables based causal discovery algorithm. First, it introduced multiset canonical correlation variables to establish a linear non-Gaussian acyclic model for them, and proposed a corresponding objective function. Then, it used the gradient as cent method to solve the objective function, and constructed a causal network over multiset canonical correlation variables. Simulation experiments verify the correctness and effectiveness of the algorithm, and find a number of valuable sets of wireless network performance indicators on the mobile base station dataset. |
| 英文关键词 | multiset canonical correlation variables; linear non-Gaussian acyclic model; causal discovery; causal network |