scholarly journals A Sparse Structure Learning Algorithm for Gaussian Bayesian Network Identification from High-Dimensional Data

2013 ◽  
Vol 35 (6) ◽  
pp. 1328-1342 ◽  
Author(s):  
Shuai Huang ◽  
Jing Li ◽  
Jieping Ye ◽  
Adam Fleisher ◽  
Kewei Chen ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Structure Learning ◽  
Learning Algorithm ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Network Identification ◽  
Gaussian Bayesian Network

Bayesian Network Structure Learning Based on Probability Density Kernel Estimation

2013 ◽  
Vol 427-429 ◽  
pp. 1614-1619
Author(s):  
Shao Jin Han ◽  
Jian Xun Li
Keyword(s):  
Bayesian Network ◽  
Probability Density ◽  
Network Structure ◽  
Structure Learning ◽  
Learning Algorithm ◽  
Kernel Estimation ◽  
Small Sample ◽  
Variable Order ◽  
Bayesian Network Structure ◽  
Original Dataset

The traditional structure learning algorithms are mainly faced with a large sample dataset. But the sample dataset practically is small. Based on it, we introduce the Probability Density Kernel Estimation (PDKE), which would achieve the expansion of the original sample sets. Then, the K2 algorithm is used to learn the Bayesian network structure. By optimizing the kernel function and window width, PDKE achieves the effective expansion of the original dataset. After the confirm of variable order based on mutual information, a small sample set of Bayesian structure learning algorithm would be established. Finally, simulation results confirm that the new algorithm is effective and practical.

An Effective Neural Learning Algorithm for Extracting Cross-Correlation Feature Between Two High-Dimensional Data Streams

2014 ◽  
Vol 42 (2) ◽  
pp. 459-477 ◽  
Author(s):  
Xiang-yu Kong ◽  
Hong-guang Ma ◽  
Qiu-sheng An ◽  
Qi Zhang
Keyword(s):  
Data Streams ◽  
Cross Correlation ◽  
Learning Algorithm ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Neural Learning

scholarly journals Dynamic Programming Structure Learning Algorithm of Bayesian Network Integrating MWST and Improved MMPC

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Ruo-Hai Di ◽  
Ye Li ◽  
Ting-Peng Li ◽  
Lian-Dong Wang ◽  
Peng Wang
Keyword(s):  
Dynamic Programming ◽  
Bayesian Network ◽  
Spanning Tree ◽  
Large Scale ◽  
Structure Learning ◽  
Learning Algorithm ◽  
Maximum Weight ◽  
Significance Level ◽  
Bayesian Network Structure ◽  
Parents And Children

Dynamic programming is difficult to apply to large-scale Bayesian network structure learning. In view of this, this article proposes a BN structure learning algorithm based on dynamic programming, which integrates improved MMPC (maximum-minimum parents and children) and MWST (maximum weight spanning tree). First, we use the maximum weight spanning tree to obtain the maximum number of parent nodes of the network node. Second, the MMPC algorithm is improved by the symmetric relationship to reduce false-positive nodes and obtain the set of candidate parent-child nodes. Finally, with the maximum number of parent nodes and the set of candidate parent nodes as constraints, we prune the parent graph of dynamic programming to reduce the number of scoring calculations and the complexity of the algorithm. Experiments have proved that when an appropriate significance level α is selected, the MMPCDP algorithm can greatly reduce the number of scoring calculations and running time while ensuring its accuracy.

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