On supervised graph Laplacian embedding CA model & kernel construction and its application. Issue 1 (2nd January 2017)

• Record Type: Journal Article
• Authors: Zeng, Junwei; Qian, Yongsheng; Wang, Min; Yang, Yongzhong
• Is Part Of: Journal of experimental & theoretical artificial intelligence. Volume 29:Issue 1(2017)
• Page Start: 81
• Page End: 89
• Publication Date: 2017-01-02
• Subjects: Graph Laplacian -- embedding -- applications -- cellular automaton model -- simulation; Artificial intelligence -- Periodicals; 006.3
• DOI: 10.1080/0952813X.2015.1132259
• Languages: English
• ISSNs: 0952-813X

Abstract

Abstract: There are many methods to construct kernel with given data attribute information. Gaussian radial basis function (RBF) kernel is one of the most popular ways to construct a kernel. The key observation is that in real-world data, besides the data attribute information, data label information also exists, which indicates the data class. In order to make use of both data attribute information and data label information, in this work, we propose a supervised kernel construction method. Supervised information from training data is integrated into standard kernel construction process to improve the discriminative property of resulting kernel. A supervised Laplacian embedding cellular automaton model is another key application developed for two-lane heterogeneous traffic flow with the safe distance and large-scale truck. Based on the properties of traffic flow in China, we re-calibrate the cell length, velocity, random slowing mechanism and lane-change conditions and use simulation tests to study the relationships among the speed, density and flux. The numerical results show that the large-scale trucks will have great effects on the traffic flow, which are relevant to the proportion of the large-scale trucks, random slowing rate and the times of the lane space change.